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Wikipedia:WikiProject Conservatism

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    Welcome to WikiProject Conservatism! Whether you're a newcomer or regular, you'll receive encouragement and recognition for your achievements with conservatism-related articles. This project does not extol any point of view, political or otherwise, other than that of a neutral documentarian. Partly due to this, the project's scope has long become that of conservatism broadly construed, taking in a healthy periphery of (e.g., more academic) articles for contextualization.

    Major alerts

    A broad collection of discussions that could lead to significant changes of related articles

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    Articles for deletion

    Proposed deletions

    Categories for discussion

    Templates for discussion

    Redirects for discussion

    Good article nominees

    Requests for comments

    Peer reviews

    Requested moves

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    Articles to be merged

    Articles to be split

    Articles for creation

    Watchlists

    WatchAll (Excerpt)
    Excerpt from watchlist concerning all the articles in the project's scope
    Note that your own edits, minor edits, and bot edits are hidden in this tab

    List of abbreviations (help):
    D
    Edit made at Wikidata
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    N
    New page
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    29 March 2025

    28 March 2025

    27 March 2025

    26 March 2025

    For this watchlist but about 3X in length, visit: Wikipedia:WikiProject Conservatism/All recent changes
    WatchHot (Excerpt)
    A list of 10 related articles with the most (recent) edits total
    383 edits Department of Government Efficiency
    187 edits March 2025 Venezuelan deportations
    105 edits Mia Love
    91 edits Donald Trump
    84 edits Pete Hegseth
    80 edits Mike Waltz
    79 edits Second presidency of Donald Trump
    75 edits Political appointments of the second Trump administration
    60 edits Republican Party (United States)
    57 edits Project 2025

    These are the articles that have been edited the most within the last seven days. Last updated 28 March 2025 by HotArticlesBot.



    List of abbreviations (help):
    D
    Edit made at Wikidata
    r
    Edit flagged by ORES
    N
    New page
    m
    Minor edit
    b
    Bot edit
    (±123)
    Page byte size change

    29 March 2025

    28 March 2025

    27 March 2025

    For this watchlist but about 5X in length, visit: Wikipedia:WikiProject Conservatism/Hot articles recent changes
    WatchPop (Excerpt)
    A list of 500 related articles with the most (recent) views total

    This is a list of pages in the scope of Wikipedia:WikiProject Conservatism along with pageviews.

    To report bugs, please write on the Community tech bot talk page on Meta.

    List

    Period: 2025-02-01 to 2025-02-28

    Total views: 88,554,132

    Updated: 10:21, 6 March 2025 (UTC)

    Rank Page title Views Daily average Assessment Importance
    1 Elon Musk 5,749,993 205,356 GA Low
    2 Donald Trump 2,721,591 97,199 B High
    3 Karoline Leavitt 2,177,644 77,773 C Unknown
    4 Department of Government Efficiency 1,932,282 69,010 B High
    5 JD Vance 1,291,978 46,142 B Mid
    6 Pam Bondi 946,791 33,813 C Low
    7 Pete Hegseth 896,170 32,006 B Low
    8 Alternative for Germany 893,152 31,898 C Low
    9 Dan Bongino 837,031 29,893 C Mid
    10 Project 2025 734,810 26,243 B Mid
    11 Alice Weidel 694,334 24,797 C Low
    12 Marco Rubio 682,585 24,378 B Mid
    13 Friedrich Merz 628,677 22,452 C Mid
    14 Kristi Noem 567,013 20,250 B Low
    15 Curtis Yarvin 561,027 20,036 C High
    16 Vladimir Putin 503,450 17,980 B High
    17 Mitch McConnell 467,962 16,712 B Mid
    18 Family of Donald Trump 459,723 16,418 B Low
    19 Ronald Reagan 442,222 15,793 FA Top
    20 Candace Owens 431,270 15,402 B Low
    21 CDU/CSU 405,303 14,475 C Low
    22 Theodore Roosevelt 389,172 13,899 B High
    23 Benjamin Netanyahu 380,012 13,571 B Mid
    24 Russell Vought 374,437 13,372 Start Mid
    25 George W. Bush 369,069 13,181 B High
    26 Winston Churchill 355,270 12,688 GA Top
    27 Linda McMahon 354,046 12,644 B Low
    28 Republican Party (United States) 324,552 11,591 B Top
    29 Narendra Modi 320,799 11,457 GA Top
    30 Richard Nixon 319,585 11,413 FA High
    31 Nayib Bukele 317,700 11,346 GA Low
    32 George H. W. Bush 316,357 11,298 B High
    33 Sahra Wagenknecht Alliance 310,001 11,071 Start Unknown
    34 William McKinley 304,672 10,881 FA Low
    35 Steve Bannon 304,322 10,868 B Mid
    36 Rupert Murdoch 302,528 10,804 B Low
    37 Mel Gibson 300,323 10,725 B Mid
    38 Christian Democratic Union of Germany 291,221 10,400 C High
    39 Nancy Mace 288,220 10,293 B Low
    40 Dwight D. Eisenhower 285,606 10,200 B High
    41 Angela Merkel 274,291 9,796 GA High
    42 Javier Milei 266,122 9,504 B Mid
    43 Dark Enlightenment 264,499 9,446 Start Mid
    44 Tom Homan 264,321 9,440 Start Low
    45 Stephen Miller (political advisor) 260,080 9,288 B Low
    46 Margaret Thatcher 249,907 8,925 A Top
    47 Woke 243,983 8,713 B Top
    48 Rishi Sunak 243,677 8,702 B High
    49 Gerald Ford 237,760 8,491 C High
    50 Bharatiya Janata Party 230,710 8,239 GA Top
    51 Free Democratic Party (Germany) 223,508 7,982 C Mid
    52 John Kennedy (Louisiana politician) 219,674 7,845 C Low
    53 Second presidency of Donald Trump 212,157 7,577 C Low
    54 Anna Paulina Luna 212,068 7,573 B Low
    55 Kemi Badenoch 198,133 7,076 B Low
    56 Lara Trump 197,084 7,038 C Low
    57 Robert Duvall 196,755 7,026 B Low
    58 Red states and blue states 194,041 6,930 C Mid
    59 Jordan Peterson 188,507 6,732 B Low
    60 Michael Waltz 187,880 6,710 Start Low
    61 Cold War 186,037 6,644 C Top
    62 French Revolution 185,661 6,630 B Top
    63 Doug Ford 183,807 6,564 B Low
    64 Zionism 181,917 6,497 B Low
    65 John Wayne 177,820 6,350 B Low
    66 Richard Grenell 176,717 6,311 C Low
    67 John Rocker 174,232 6,222 C Unknown
    68 Megyn Kelly 173,027 6,179 B Low
    69 Grover Cleveland 172,984 6,178 FA Mid
    70 Jon Voight 170,351 6,083 C Low
    71 Hillbilly Elegy 167,698 5,989 B Low
    72 Dan Crenshaw 166,922 5,961 B Low
    73 Rachel Campos-Duffy 164,986 5,892 Start Low
    74 Matt Gaetz 161,246 5,758 B Low
    75 Fyodor Dostoevsky 159,481 5,695 B Low
    76 Kayleigh McEnany 158,881 5,674 C Low
    77 Fourteen Words 155,786 5,563 Start Low
    78 Herbert Hoover 155,748 5,562 B Mid
    79 Brooke Rollins 155,402 5,550 Start Low
    80 Chuck Norris 155,361 5,548 B Low
    81 Charlie Kirk 155,192 5,542 C Low
    82 QAnon 154,755 5,526 GA Mid
    83 Marjorie Taylor Greene 151,334 5,404 GA Low
    84 Reform UK 148,860 5,316 C High
    85 Conservative Political Action Conference 146,494 5,231 C Mid
    86 Dick Cheney 144,446 5,158 C Mid
    87 Constitution of the United States 144,271 5,152 B High
    88 John Ratcliffe 143,771 5,134 C Low
    89 George Santos 143,131 5,111 B Low
    90 Marc Andreessen 140,635 5,022 C Mid
    91 Ron DeSantis 139,058 4,966 B Mid
    92 John Malkovich 136,273 4,866 C Low
    93 Boris Johnson 135,801 4,850 B High
    94 Trump derangement syndrome 133,940 4,783 C Mid
    95 Mike Pence 133,506 4,768 B Mid
    96 Byron Donalds 133,392 4,764 C Low
    97 Chuck Grassley 133,301 4,760 C Mid
    98 Mike Johnson 133,087 4,753 C Mid
    99 Neville Chamberlain 132,351 4,726 FA Mid
    100 Susie Wiles 131,626 4,700 C Low
    101 Kelsey Grammer 131,394 4,692 B Low
    102 Lauren Boebert 129,104 4,610 B Low
    103 Views of Elon Musk 127,939 4,569 B Mid
    104 Warren G. Harding 127,478 4,552 FA Low
    105 Calvin Coolidge 126,250 4,508 FA High
    106 James A. Garfield 125,949 4,498 FA Low
    107 Nick Fuentes 125,464 4,480 B Low
    108 William Howard Taft 125,220 4,472 FA Mid
    109 Tucker Carlson 124,805 4,457 B High
    110 Nigel Farage 124,301 4,439 B Mid
    111 Elon Musk salute controversy 124,101 4,432 B Low
    112 Ben Shapiro 123,583 4,413 C Mid
    113 Stephen Baldwin 123,098 4,396 B Low
    114 Chiang Kai-shek 122,552 4,376 C Low
    115 Liz Truss 122,337 4,369 FA Mid
    116 Francisco Franco 121,734 4,347 C Mid
    117 Sheldon Adelson 121,408 4,336 C Low
    118 James Caan 121,188 4,328 C Low
    119 Jesse Watters 120,893 4,317 Start Low
    120 Ayn Rand 120,182 4,292 GA Mid
    121 Riley Gaines 119,753 4,276 B Mid
    122 John Major 118,206 4,221 B High
    123 Clark Gable 118,179 4,220 B Low
    124 Far-right politics 115,567 4,127 B Low
    125 Viktor Orbán 114,533 4,090 C Mid
    126 Falun Gong 114,481 4,088 B Mid
    127 Lee Zeldin 114,052 4,073 B Low
    128 False or misleading statements by Donald Trump 113,877 4,067 B Low
    129 Sean Hannity 113,631 4,058 B Mid
    130 Charles de Gaulle 113,365 4,048 B Mid
    131 Trumpism 113,157 4,041 B Mid
    132 Karl Malone 113,144 4,040 Start Low
    133 Jeanine Pirro 113,004 4,035 B Low
    134 Imran Khan 112,255 4,009 B Low
    135 Condoleezza Rice 112,052 4,001 B Mid
    136 Dana Perino 111,235 3,972 C Low
    137 Shinzo Abe 111,169 3,970 B Mid
    138 Laura Ingraham 110,843 3,958 C Mid
    139 Shirley Temple 110,584 3,949 B Low
    140 Milo Yiannopoulos 110,047 3,930 C Low
    141 Mitt Romney 109,631 3,915 FA High
    142 Elise Stefanik 109,169 3,898 B Low
    143 Fox News 108,312 3,868 C Mid
    144 Paul von Hindenburg 106,697 3,810 C Mid
    145 Proud Boys 106,641 3,808 C Low
    146 John McCain 105,851 3,780 FA Mid
    147 Lindsey Graham 105,851 3,780 C Low
    148 James Stewart 105,224 3,758 GA Low
    149 Doug Collins (politician) 102,988 3,678 Start Low
    150 Recep Tayyip Erdoğan 101,716 3,632 B High
    151 Kelly Loeffler 100,680 3,595 B Low
    152 The Heritage Foundation 100,585 3,592 B High
    153 Greg Abbott 100,469 3,588 B Mid
    154 Greg Gutfeld 100,348 3,583 C Low
    155 Mike Gabbard 100,276 3,581 C Low
    156 Otto von Bismarck 100,059 3,573 B High
    157 History of tariffs in the United States 99,643 3,558 B Mid
    158 Anders Behring Breivik 99,066 3,538 C Low
    159 Patricia Heaton 98,275 3,509 C Low
    160 Deep state conspiracy theory in the United States 97,674 3,488 Start Low
    161 Libertarianism 97,465 3,480 B High
    162 Deng Xiaoping 96,813 3,457 B Low
    163 Conservative Party (UK) 95,901 3,425 B High
    164 Brett Cooper (commentator) 95,801 3,421 Start Low
    165 Tommy Tuberville 95,473 3,409 B Low
    166 Thomas Sowell 94,722 3,382 C Mid
    167 Ben Carson 94,541 3,376 C Low
    168 Ashley Moody 94,281 3,367 C Unknown
    169 Elaine Chao 93,989 3,356 B Low
    170 Clarence Thomas 93,904 3,353 B Mid
    171 Bill Cassidy 93,317 3,332 C Mid
    172 Benjamin Harrison 93,167 3,327 FA Low
    173 1964 United States presidential election 93,109 3,325 C Mid
    174 Morgan Ortagus 92,606 3,307 C Unknown
    175 John Roberts 92,354 3,298 B High
    176 Victor Davis Hanson 92,268 3,295 B Mid
    177 Călin Georgescu 92,142 3,290 C Low
    178 Ron Paul 92,048 3,287 C Mid
    179 Taliban 91,927 3,283 B High
    180 Christian Social Union in Bavaria 90,659 3,237 C Mid
    181 Ted Cruz 90,184 3,220 B Mid
    182 Dmitry Medvedev 90,097 3,217 C High
    183 Critical race theory 89,771 3,206 C Low
    184 McCarthyism 89,447 3,194 C High
    185 James Woods 89,300 3,189 Start Low
    186 Chester A. Arthur 88,711 3,168 FA Low
    187 Neoliberalism 88,028 3,143 B Top
    188 Shigeru Ishiba 87,120 3,111 B Low
    189 Truth Social 87,017 3,107 B Low
    190 Rand Paul 85,722 3,061 GA Mid
    191 Arthur Wellesley, 1st Duke of Wellington 85,131 3,040 B Low
    192 Bing Crosby 84,929 3,033 B Low
    193 Ted Nugent 84,883 3,031 C Low
    194 Peggy Noonan 84,789 3,028 C Low
    195 Booker T. Washington 84,759 3,027 B Low
    196 Rudy Giuliani 84,660 3,023 B Mid
    197 Charlton Heston 84,494 3,017 B Low
    198 Generation 84,461 3,016 B Mid
    199 Make America Great Again 83,870 2,995 B High
    200 Donald Rumsfeld 82,186 2,935 B Mid
    201 Craig T. Nelson 82,007 2,928 Start Unknown
    202 Conservative Party of Canada 81,984 2,928 B High
    203 Political appointments of the second Trump administration 81,553 2,912 List Low
    204 Charles Lindbergh 81,544 2,912 B Low
    205 Foundations of Geopolitics 81,477 2,909 C Unknown
    206 Rashtriya Swayamsevak Sangh 80,874 2,888 C Top
    207 Iran–Contra affair 79,851 2,851 GA Low
    208 Sarah Palin 79,550 2,841 C Mid
    209 Mary Matalin 79,047 2,823 C Low
    210 Marko Elez 78,980 2,820 Start Low
    211 Whig Party (United States) 78,734 2,811 C Low
    212 Dennis Miller 78,690 2,810 Start Low
    213 John Thune 78,553 2,805 C Low
    214 John Locke 78,036 2,787 B Top
    215 Michael Steele 76,885 2,745 B Low
    216 Muhammad Ali Jinnah 76,765 2,741 FA High
    217 Left–right political spectrum 76,085 2,717 C Top
    218 Unitary executive theory 75,540 2,697 C Mid
    219 Denis Leary 74,704 2,668 C NA
    220 David Cameron 74,575 2,663 B Top
    221 Groypers 74,385 2,656 B Low
    222 The Epoch Times 74,239 2,651 B Low
    223 Mark Rutte 74,219 2,650 C High
    224 David Duke 74,060 2,645 B Mid
    225 George Wallace 73,786 2,635 B Mid
    226 Nancy Reagan 73,298 2,617 B Mid
    227 Liz Cheney 73,262 2,616 B High
    228 Gary Cooper 72,794 2,599 FA Mid
    229 Gadsden flag 72,401 2,585 B Low
    230 Right-wing politics 72,024 2,572 C Top
    231 Jair Bolsonaro 70,705 2,525 B Mid
    232 Last Man Standing (American TV series) 70,595 2,521 B Low
    233 Deportation of illegal immigrants in the second presidency of Donald Trump 70,405 2,514 B Low
    234 Right-wing populism 70,242 2,508 B High
    235 Rutherford B. Hayes 70,199 2,507 FA Low
    236 Milton Friedman 69,701 2,489 GA High
    237 T. S. Eliot 69,517 2,482 B Low
    238 Bo Derek 68,744 2,455 Start Low
    239 Steele dossier 68,224 2,436 B Low
    240 Progressive Conservative Party of Ontario 68,141 2,433 B Mid
    241 Daily Mail 67,959 2,427 B Mid
    242 Sarah Huckabee Sanders 67,735 2,419 C Low
    243 Atal Bihari Vajpayee 67,365 2,405 GA High
    244 Jack Posobiec 67,306 2,403 B Low
    245 Rush Limbaugh 66,975 2,391 B High
    246 Anthony Eden 66,816 2,386 B Mid
    247 Stephen Harper 66,700 2,382 GA High
    248 Tony Hinchcliffe 66,470 2,373 B Low
    249 First presidency of Donald Trump 65,448 2,337 B Low
    250 Kellyanne Conway 64,637 2,308 B Low
    251 Will Cain 64,582 2,306 Start Mid
    252 Great Replacement conspiracy theory 64,544 2,305 C Top
    253 Spiro Agnew 64,534 2,304 FA Mid
    254 Angie Harmon 64,397 2,299 C Low
    255 Jackson Hinkle 64,330 2,297 B Low
    256 Nikki Haley 63,862 2,280 B Low
    257 Gary Sinise 62,601 2,235 C Low
    258 Mike DeWine 62,257 2,223 B Low
    259 Joe Kent 62,184 2,220 C Low
    260 Roger Stone 61,927 2,211 C Low
    261 Vinayak Damodar Savarkar 61,885 2,210 B High
    262 Katie Britt 61,815 2,207 C Low
    263 Thomas Massie 61,363 2,191 B Low
    264 Theresa May 60,691 2,167 B Mid
    265 Benjamin Disraeli 60,436 2,158 FA Top
    266 John Bolton 60,348 2,155 C Mid
    267 Conservatism 60,162 2,148 B Top
    268 Dave Mustaine 60,087 2,145 C Low
    269 Tom Cotton 60,033 2,144 C Low
    270 Lisa Murkowski 60,030 2,143 C High
    271 Patrick Bet-David 59,479 2,124 C Low
    272 Manosphere 59,465 2,123 C Low
    273 Michael Knowles (political commentator) 59,339 2,119 Start Low
    274 Breitbart News 59,286 2,117 C Mid
    275 Rick Scott 59,226 2,115 C Low
    276 First impeachment of Donald Trump 58,718 2,097 B High
    277 Strom Thurmond 58,288 2,081 B Mid
    278 Christian nationalism 58,053 2,073 Start High
    279 Dan Quayle 57,987 2,070 B Mid
    280 White supremacy 57,883 2,067 B Low
    281 House of Bourbon 57,881 2,067 B High
    282 Douglas Murray (author) 57,808 2,064 C Low
    283 Agenda 47 57,695 2,060 C Top
    284 Donald Trump and fascism 56,900 2,032 B Mid
    285 Rick Perry 56,620 2,022 B Mid
    286 Capitalism 56,272 2,009 C Top
    287 Brothers of Italy 56,138 2,004 B Mid
    288 Dinesh D'Souza 56,112 2,004 B Mid
    289 Pat Sajak 55,689 1,988 C Low
    290 Edward Teller 55,055 1,966 FA Low
    291 Robert Davi 54,858 1,959 Start Low
    292 Oliver North 54,687 1,953 C Mid
    293 Tom Clancy 54,511 1,946 C Low
    294 Anthony Scaramucci 54,341 1,940 C Low
    295 Matt Walsh (political commentator) 54,120 1,932 C Low
    296 Nicolas Sarkozy 54,047 1,930 B High
    297 The Wall Street Journal 53,997 1,928 B Mid
    298 National Rally 53,991 1,928 GA High
    299 Tomi Lahren 53,850 1,923 Start Low
    300 Nick Land 53,776 1,920 C Low
    301 Deus vult 53,382 1,906 Start Low
    302 Tim Montgomerie 53,348 1,905 C Mid
    303 Barbara Stanwyck 53,114 1,896 B Low
    304 Harold Macmillan 52,635 1,879 B High
    305 Adam Kinzinger 52,529 1,876 C Low
    306 Paul Ryan 52,275 1,866 C Mid
    307 Aleksandr Dugin 51,968 1,856 C Mid
    308 Brett Kavanaugh 51,870 1,852 B High
    309 Newt Gingrich 51,317 1,832 GA High
    310 Thom Tillis 51,246 1,830 B Low
    311 Franz von Papen 51,031 1,822 B Low
    312 Barry Goldwater 50,804 1,814 B High
    313 Luke Farritor 50,794 1,814 Start Low
    314 New Blue Party of Ontario 50,387 1,799 Start Low
    315 Tea Party movement 49,650 1,773 C Mid
    316 W. B. Yeats 49,516 1,768 FA Low
    317 James Cagney 49,486 1,767 B Low
    318 Zora Neale Hurston 49,282 1,760 B Low
    319 Betsy DeVos 49,150 1,755 C Mid
    320 Stacey Dash 49,067 1,752 C Low
    321 Mark Levin 49,064 1,752 B High
    322 Tim Scott 48,792 1,742 C Low
    323 Kevin McCarthy 48,759 1,741 C Low
    324 Marine Le Pen 48,361 1,727 B Low
    325 Joni Ernst 48,347 1,726 B Low
    326 Patriots for Europe 48,258 1,723 C Low
    327 Helmut Kohl 48,106 1,718 B High
    328 Federalist Party 48,052 1,716 C Low
    329 Bob Hope 47,919 1,711 B Low
    330 Victoria Jackson 47,876 1,709 Start Low
    331 Melissa Joan Hart 47,841 1,708 B Low
    332 Jeb Bush 47,392 1,692 B Low
    333 Bill O'Reilly (political commentator) 47,176 1,684 B Mid
    334 L. K. Advani 47,142 1,683 B High
    335 United Russia 46,734 1,669 B High
    336 Rumble (company) 46,334 1,654 Start Low
    337 Bill Kristol 46,296 1,653 B High
    338 Ann Coulter 46,216 1,650 B Mid
    339 Antonin Scalia 46,113 1,646 FA High
    340 Executive Order 14168 46,066 1,645 C Low
    341 Amy Coney Barrett 45,854 1,637 C Low
    342 Neoconservatism 45,802 1,635 C Top
    343 Kevin Hassett 45,802 1,635 Start Mid
    344 Buddy Carter 45,756 1,634 Start Low
    345 Sebastian Gorka 45,185 1,613 C Unknown
    346 Dave Ramsey 45,174 1,613 C Unknown
    347 Frank Bruno 45,062 1,609 Start Unknown
    348 Reagan (2024 film) 44,836 1,601 C Low
    349 Bob Dole 44,744 1,598 B Low
    350 The Fountainhead 44,179 1,577 FA Low
    351 Ginger Rogers 44,169 1,577 C Unknown
    352 Laura Bush 44,115 1,575 GA Low
    353 Ray Bradbury 43,926 1,568 B Low
    354 Martin Heidegger 43,793 1,564 C Low
    355 Terri Schiavo case 43,742 1,562 GA Low
    356 1924 United States presidential election 43,509 1,553 C Low
    357 Jacobitism 43,483 1,552 B High
    358 Björn Höcke 43,421 1,550 Start Low
    359 Chad Bianco 43,409 1,550 Start Low
    360 Meir Kahane 43,275 1,545 B High
    361 Edward Heath 43,107 1,539 B High
    362 Lawrence B. Jones 43,013 1,536 Start Unknown
    363 Mike Huckabee 42,907 1,532 B Mid
    364 Phil Robertson 42,733 1,526 C Low
    365 Marsha Blackburn 42,701 1,525 C Low
    366 John C. Calhoun 42,622 1,522 FA Top
    367 Liberal Democratic Party (Japan) 42,433 1,515 C High
    368 Scott Baio 42,341 1,512 Start Low
    369 Aleksandr Solzhenitsyn 42,322 1,511 B Mid
    370 New York Post 42,049 1,501 C Low
    371 Eduardo Verástegui 42,032 1,501 C Mid
    372 David Mamet 41,796 1,492 C Low
    373 Tammy Bruce 41,417 1,479 Start Low
    374 Jack Kemp 41,374 1,477 GA Mid
    375 The Times of India 41,254 1,473 C Mid
    376 Stephanie Grisham 41,246 1,473 C Low
    377 Barack Obama citizenship conspiracy theories 41,239 1,472 B Low
    378 Brian Mast 41,018 1,464 C Low
    379 Roger Wicker 40,885 1,460 C Mid
    380 Jane Russell 40,800 1,457 B Low
    381 John Layfield 40,625 1,450 B Low
    382 The Daily Telegraph 40,582 1,449 C Low
    383 Alpha and beta male 40,557 1,448 C Low
    384 Jeff Landry 40,482 1,445 C Low
    385 Kari Lake 40,458 1,444 C Low
    386 Laura Loomer 40,043 1,430 C Low
    387 White Horse Prophecy 39,962 1,427 GA Low
    388 Likud 39,884 1,424 C Low
    389 Alt-right 39,860 1,423 C Mid
    390 Illegal immigration to the United States 39,811 1,421 B High
    391 Danielle Smith 39,720 1,418 B Unknown
    392 Ustaše 39,557 1,412 C High
    393 William Barr 39,409 1,407 B Unknown
    394 Laissez-faire 39,229 1,401 C Top
    395 Donald Trump 2024 presidential campaign 39,159 1,398 B Low
    396 Michael Farmer, Baron Farmer 39,091 1,396 C Low
    397 Friedrich Hayek 38,954 1,391 B Top
    398 Neil Gorsuch 38,929 1,390 B Mid
    399 Conservatism in the United States 38,721 1,382 B Top
    400 John Birch Society 38,662 1,380 C Low
    401 Mahathir Mohamad 38,503 1,375 GA High
    402 Mike Lindell 38,318 1,368 C Low
    403 Winsome Earle-Sears 38,037 1,358 C Low
    404 Twitter under Elon Musk 38,030 1,358 B Mid
    405 John Cornyn 37,730 1,347 B Low
    406 Geert Wilders 37,414 1,336 B Low
    407 Devin Nunes 37,367 1,334 C Low
    408 The Daily Wire 37,222 1,329 C Low
    409 Ayaan Hirsi Ali 36,967 1,320 B Low
    410 Chris Christie 36,917 1,318 C Low
    411 Benny Johnson (columnist) 36,783 1,313 Start Low
    412 William F. Buckley Jr. 36,701 1,310 B Top
    413 Reform Party of the United States of America 36,675 1,309 C Low
    414 Fairness doctrine 36,623 1,307 C Mid
    415 Pat Buchanan 36,575 1,306 B Mid
    416 People's Party of Canada 36,468 1,302 C Low
    417 Law and Justice 36,443 1,301 C High
    418 Alexander Gauland 36,369 1,298 Start Mid
    419 Anarcho-capitalism 36,356 1,298 B Low
    420 Mike Lee 36,254 1,294 C Low
    421 Edmund Burke 36,233 1,294 B Top
    422 Austrian People's Party 36,161 1,291 C High
    423 Ulf Kristersson 36,134 1,290 B Low
    424 White genocide conspiracy theory 36,109 1,289 B Low
    425 Fianna Fáil 35,933 1,283 B Low
    426 Edward Coristine 35,781 1,277 Redirect Low
    427 Pat Boone 35,720 1,275 C Low
    428 Park Chung Hee 35,605 1,271 C Low
    429 Fred MacMurray 35,454 1,266 C Low
    430 Hillsdale College 35,321 1,261 C Low
    431 Libs of TikTok 35,277 1,259 B Low
    432 Classical liberalism 35,096 1,253 B Top
    433 Brian Mulroney 34,905 1,246 B High
    434 Mullah Omar 34,903 1,246 B High
    435 Matt Schlapp 34,640 1,237 C Low
    436 Anita Bryant 34,636 1,237 B High
    437 Éamon de Valera 34,594 1,235 B High
    438 Progressivism 34,506 1,232 C Mid
    439 German National People's Party 34,490 1,231 Start Mid
    440 Christian democracy 34,481 1,231 B Top
    441 D. H. Lawrence 34,469 1,231 B Unknown
    442 David Koch 34,453 1,230 C Mid
    443 The Gateway Pundit 34,448 1,230 C Unknown
    444 Jim Jordan 34,387 1,228 B Low
    445 Jacob Rees-Mogg 34,308 1,225 C Low
    446 Christopher Luxon 34,306 1,225 B Unknown
    447 Gavin McInnes 34,232 1,222 C Low
    448 Todd Young 34,185 1,220 Start Low
    449 Fred Thompson 34,165 1,220 B Low
    450 Don King 34,075 1,216 B Low
    451 Joe Wilson (American politician) 34,039 1,215 C Low
    452 UK Independence Party 33,910 1,211 B Low
    453 Corey Lewandowski 33,900 1,210 C Low
    454 Jeff Sessions 33,634 1,201 Start Unknown
    455 12 Rules for Life 33,581 1,199 B Mid
    456 Steve Doocy 33,484 1,195 Start Unknown
    457 Kalergi Plan 33,305 1,189 Start Mid
    458 Bourbon Restoration in France 33,302 1,189 C High
    459 Alliance for Responsible Citizenship 33,019 1,179 Unknown Unknown
    460 Mercedes Schlapp 32,942 1,176 C Low
    461 Thomas Mann 32,882 1,174 C Mid
    462 New Flemish Alliance 32,822 1,172 C Low
    463 António de Oliveira Salazar 32,531 1,161 B Mid
    464 Jordan Bardella 32,482 1,160 C High
    465 Lee Hsien Loong 32,381 1,156 C Mid
    466 Menachem Begin 32,192 1,149 B Mid
    467 Vox (political party) 32,101 1,146 B Mid
    468 H. L. Hunt 32,058 1,144 Start Low
    469 Flannery O'Connor 31,902 1,139 A Low
    470 Enoch Powell 31,806 1,135 C High
    471 Party for Freedom 31,738 1,133 C Mid
    472 Claudia Tenney 31,722 1,132 B Low
    473 Political spectrum 31,575 1,127 C Top
    474 Primogeniture 31,509 1,125 Start Low
    475 Deportation and removal from the United States 31,471 1,123 C Unknown
    476 Samuel Alito 31,371 1,120 C Mid
    477 Europe of Sovereign Nations Group 31,348 1,119 C High
    478 William Hague 31,210 1,114 C High
    479 Twitter Files 31,115 1,111 C Low
    480 Alec Douglas-Home 31,052 1,109 FA Low
    481 Richard B. Spencer 31,002 1,107 C Low
    482 Political activities of Elon Musk 30,995 1,106 C Low
    483 Larry Sanger 30,963 1,105 B Low
    484 Walter Brennan 30,816 1,100 C Low
    485 Ben Stein 30,776 1,099 C Low
    486 Centre Party (Germany) 30,766 1,098 C Mid
    487 Facebook–Cambridge Analytica data scandal 30,724 1,097 C Unknown
    488 2016 Republican Party presidential primaries 30,717 1,097 B Mid
    489 Glenn Beck 30,687 1,095 B Mid
    490 Meghan McCain 30,596 1,092 C Low
    491 Alessandra Mussolini 30,522 1,090 B Low
    492 Liberty University 30,439 1,087 B Mid
    493 Shiv Sena 30,414 1,086 C Unknown
    494 Roger Ailes 30,310 1,082 C Mid
    495 Helen Hayes 30,261 1,080 B Low
    496 William Rehnquist 30,222 1,079 B High
    497 Infowars 30,171 1,077 C Low
    498 Carl Schmitt 30,154 1,076 C Top
    499 Nawaz Sharif 30,128 1,076 B Unknown
    500 Newsmax 30,045 1,073 B Low


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    In The Signpost

    One of various articles to this effect
    The Right Stuff
    July 2018
    DISCUSSION REPORT
    WikiProject Conservatism Comes Under Fire

    By Lionelt

    WikiProject Conservatism was a topic of discussion at the Administrators' Noticeboard/Incident (AN/I). Objective3000 started a thread where he expressed concern regarding the number of RFC notices posted on the Discussion page suggesting that such notices "could result in swaying consensus by selective notification." Several editors participated in the relatively abbreviated six hour discussion. The assertion that the project is a "club for conservatives" was countered by editors listing examples of users who "profess no political persuasion." It was also noted that notification of WikiProjects regarding ongoing discussions is explicitly permitted by the WP:Canvassing guideline.

    At one point the discussion segued to feedback about The Right Stuff. Member SPECIFICO wrote: "One thing I enjoy about the Conservatism Project is the handy newsletter that members receive on our talk pages." Atsme praised the newsletter as "first-class entertainment...BIGLY...first-class...nothing even comes close...it's amazing." Some good-natured sarcasm was offered with Objective3000 observing, "Well, they got the color right" and MrX's followup, "Wow. Yellow is the new red."

    Admin Oshwah closed the thread with the result "definitely not an issue for ANI" and directing editors to the project Discussion page for any further discussion. Editor's note: originally the design and color of The Right Stuff was chosen to mimic an old, paper newspaper.

    Add the Project Discussion page to your watchlist for the "latest RFCs" at WikiProject Conservatism Watch (Discuss this story)

    ARTICLES REPORT
    Margaret Thatcher Makes History Again

    By Lionelt

    Margaret Thatcher is the first article promoted at the new WikiProject Conservatism A-Class review. Congratulations to Neveselbert. A-Class is a quality rating which is ranked higher than GA (Good article) but the criteria are not as rigorous as FA (Featued article). WikiProject Conservatism is one of only two WikiProjects offering A-Class review, the other being WikiProject Military History. Nominate your article here. (Discuss this story)
    RECENT RESEARCH
    Research About AN/I

    By Lionelt

    Reprinted in part from the April 26, 2018 issue of The Signpost; written by Zarasophos

    Out of over one hundred questioned editors, only twenty-seven (27%) are happy with the way reports of conflicts between editors are handled on the Administrators' Incident Noticeboard (AN/I), according to a recent survey . The survey also found that dissatisfaction has varied reasons including "defensive cliques" and biased administrators as well as fear of a "boomerang effect" due to a lacking rule for scope on AN/I reports. The survey also included an analysis of available quantitative data about AN/I. Some notable takeaways:

    • 53% avoided making a report due to fearing it would not be handled appropriately
    • "Otherwise 'popular' users often avoid heavy sanctions for issues that would get new editors banned."
    • "Discussions need to be clerked to keep them from raising more problems than they solve."

    In the wake of Zarasophos' article editors discussed the AN/I survey at The Signpost and also at AN/I. Ironically a portion of the AN/I thread was hatted due to "off-topic sniping." To follow-up the problems identified by the research project the Wikimedia Foundation Anti-Harassment Tools team and Support and Safety team initiated a discussion. You can express your thoughts and ideas here.

    (Discuss this story)

    Delivered: ~~~~~


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    WikiProject Conservatism

    Is Wikipedia Politically Biased? Perhaps


    A monthly overview of recent academic research about Wikipedia and other Wikimedia projects, also published as the Wikimedia Research Newsletter.


    Report by conservative think-tank presents ample quantitative evidence for "mild to moderate" "left-leaning bias" on Wikipedia

    A paper titled "Is Wikipedia Politically Biased?"[1] answers that question with a qualified yes:

    [...] this report measures the sentiment and emotion with which political terms are used in [English] Wikipedia articles, finding that Wikipedia entries are more likely to attach negative sentiment to terms associated with a right-leaning political orientation than to left-leaning terms. Moreover, terms that suggest a right-wing political stance are more frequently connected with emotions of anger and disgust than those that suggest a left-wing stance. Conversely, terms associated with left-leaning ideology are more frequently linked with the emotion of joy than are right-leaning terms.
    Our findings suggest that Wikipedia is not entirely living up to its neutral point of view policy, which aims to ensure that content is presented in an unbiased and balanced manner.

    The author (David Rozado, an associate professor at Otago Polytechnic) has published ample peer-reviewed research on related matters before, some of which was featured e.g. in The Guardian and The New York Times. In contrast, the present report is not peer-reviewed and was not posted in an academic venue, unlike most research we cover here usually. Rather, it was published (and possibly commissioned) by the Manhattan Institute, a conservative US think tank, which presumably found its results not too objectionable. (Also, some – broken – URLs in the PDF suggest that Manhattan Institute staff members were involved in the writing of the paper.) Still, the report indicates an effort to adhere to various standards of academic research publications, including some fairly detailed descriptions of the methods and data used. It is worth taking it more seriously than, for example, another recent report that alleged a different form of political bias on Wikipedia, which had likewise been commissioned by an advocacy organization and authored by an academic researcher, but was met with severe criticism by the Wikimedia Foundation (who called it out for "unsubstantiated claims of bias") and volunteer editors (see prior Signpost coverage).

    That isn't to say that there can't be some questions about the validity of Rozado's results, and in particular about how to interpret them. But let's first go through the paper's methods and data sources in more detail.

    Determining the sentiment and emotion in Wikipedia's coverage

    The report's main results regarding Wikipedia are obtained as follows:

    "We first gather a set of target terms (N=1,628) with political connotations (e.g., names of recent U.S. presidents, U.S. congressmembers, U.S. Supreme Court justices, or prime ministers of Western countries) from external sources. We then identify all mentions in English-language Wikipedia articles of those terms.

    We then extract the paragraphs in which those terms occur to provide the context in which the target terms are used and feed a random sample of those text snippets to an LLM (OpenAI’s gpt-3.5-turbo), which annotates the sentiment/emotion with which the target term is used in the snippet. To our knowledge, this is the first analysis of political bias in Wikipedia content using modern LLMs for annotation of sentiment/emotion."

    The sentiment classification rates the mention of a terms as negative, neutral or positive. (For the purpose of forming averages this is converted into a quantitative scale from -1 to +1.) See the end of this review for some concrete examples from the paper's published dataset.

    The emotion classification uses "Ekman’s six basic emotions (anger, disgust, fear, joy, sadness, and surprise) plus neutral."

    The annotation method used appears to be an effort to avoid the shortcomings of popular existing sentiment analysis techniques, which often only rate the overall emotional stance of a given text overall without determining whether it actually applies to a specific entity mentioned in it (or in some cases even fail to handle negations, e.g. by classifying "I am not happy" as a positive emotion). Rozado justifies the "decision to use automated annotation" (which presumably rendered considerable cost savings, also by resorting to OpenAI's older GPT 3.5 model rather than the more powerful but more expensive GPT-4 API released in March 2023) citing "recent evidence showing how top-of-the-rank LLMs outperform crowd workers for text-annotation tasks such as stance detection." This is indeed becoming a more widely used choice for text classification. But Rozado appears to have skipped the usual step of evaluating the accuracy of this automated method (and possibly improving the prompts it used) against a gold standard sample from (human) expert raters.

    Selecting topics to examine for bias

    As for the selection of terms whose Wikipedia coverage to annotate with this classifier, Rozado does a lot of due diligence to avoid cherry-picking: "To reduce the degrees of freedom of our analysis, we mostly use external sources of terms [including Wikipedia itself, e.g. its list of members of the 11th US Congress] to conceptualize a political category into left- and right-leaning terms, as well as to choose the set of terms to include in each category." This addresses an important source of researcher bias.

    Overall, the study arrives at 12 different groups of such terms:

    • 8 of these refer to people (e.g. US presidents, US senators, UK members of parliament, US journalists).
    • Two are about organizations (US think tanks and media organizations).
    • The other two groups contain "Terms that describe political orientation", i.e. expressions that carry a left-leaning or right-leaning meaning themselves:
      • 18 "political leanings" (where "Rightists" receives the lowest average sentiment and "Left winger" the highest), and
      • 21 "extreme political ideologies" (where "Ultraconservative" scores lowest and "radical-left" has the highest – but still slightly negative – average sentiment)

    What is "left-leaning" and "right-leaning"?

    As discussed, Rozado's methods for generating these lists of people and organizations seem reasonably transparent and objective. It gets a bit murkier when it comes to splitting them into "left-leaning" and "right-leaning", where the chosen methods remain unclear and/or questionable in some cases. Of course there is a natural choice available for US Congress members, where the confines of the US two-party system mean that the left-right spectrum can be easily mapped easily to Democrats vs. Republicans (disregarding a small number of independents or libertarians).

    In other cases, Rozado was able to use external data about political leanings, e.g. "a list of politically aligned U.S.-based journalists" from Politico. There may be questions about construct validity here (e.g. it classifies Glenn Greenwald or Andrew Sullivan as "journalists with the left"), but at least this data is transparent and determined by a source not invested in the present paper's findings.

    But for example the list of UK MPs used contains politicians from 14 different parties (plus independents). Even if one were to confine the left vs. right labels to the two largest groups in the UK House of Commons (Tories vs. Labour and Co-operative Party, which appears to have been the author's choice judging from Figure 5), the presence of a substantial number of parliamentarians from other parties to the left or right of those would make the validity of this binary score more questionable than in the US case. Rozado appears to acknowledge a related potential issue in a side remark when trying to offer an explanation for one of the paper's negative results (no bias) in this case: "The disparity of sentiment associations in Wikipedia articles between U.S. Congressmembers and U.K. MPs based on their political affiliation may be due in part to the higher level of polarization in the U.S. compared to the U.K."

    Tony Abbott.
    Most negative sentiment among Western leaders: Former Australian PM Tony Abbott
    Scott Morrison.
    Most positive sentiment among Western leaders: Former Australian PM Scott Morrison

    This kind of question become even more complicated for the "Leaders of Western Countries" list (where Tony Abbott scored the most negative average sentiment, and José Luis Rodríguez Zapatero and Scott Morrison appear to be in a tie for the most positive average sentiment). Most of these countries do not have a two-party system either. Sure, their leaders usually (like in the UK case) hail from one of the two largest parties, one of which is more to the left and the another more to the right. But it certainly seems to matter for the purpose of Rozado's research question whether that major party is more moderate (center-left or center-right, with other parties between it and the far left or far right) or more radical (i.e. extending all the way to the far-left or far-right spectrum of elected politicians).

    What's more, the analysis for this last group compares political orientations across multiple countries. Which brings us to a problem that Wikipedia's Jimmy Wales had already pointed to back in 2006 in response a conservative US blogger who had argued that there was "a liberal bias in many hot-button topic entries" on English Wikipedia:

    "The Wikipedia community is very diverse, from liberal to conservative to libertarian and beyond. If averages mattered, and due to the nature of the wiki software (no voting) they almost certainly don't, I would say that the Wikipedia community is slightly more liberal than the U.S. population on average, because we are global and the international community of English speakers is slightly more liberal than the U.S. population. ... The idea that neutrality can only be achieved if we have some exact demographic matchup to [the] United States of America is preposterous."

    We already discussed this issue in our earlier reviews of a notable series of papers by Greenstein and Zhu (see e.g.: "Language analysis finds Wikipedia's political bias moving from left to right", 2012), which had relied on a US-centric method of defining left-leaning and right-leaning (namely, a corpus derived from the US Congressional Record). Those studies form a large part of what Rozado cites as "[a] substantial body of literature [that]—albeit with some exceptions—has highlighted a perceived bias in Wikipedia content in favor of left-leaning perspectives." (The cited exception is a paper[2] that had found "a small to medium size coverage bias against [members of parliament] from the center-left parties in Germany and in France", and identified patterns of "partisan contributions" as a plausible cause.)

    Similarly, 8 out of the 10 groups of people and organizations analyzed in Rozado's study are from the US (the two exceptions being the aforementioned lists of UK MPs and leaders of Western countries).

    In other words, one potential reason for the disparities found by Rozado might simply be that he is measuring an international encyclopedia with a (largely) national yardstick of fairness. This shouldn't let us dismiss his findings too easily. But it is a bit disappointing that this possibility is nowhere addressed in the paper, even though Rozado diligently discusses some other potential limitations of the results. E.g. he notes that "some research has suggested that conservatives themselves are more prone to negative emotions and more sensitive to threats than liberals", but points out that the general validity of those research results remains doubtful.

    Another limitation is that a simple binary left vs. right classification might be hiding factors that can shed further light on bias findings. Even in the US with its two-party system, political scientists and analysts have long moved to less simplistic measures of political orientations. A widely used one is the NOMINATE method which assigns members of the US Congress continuous scores based on their detailed voting record, one of which corresponds to the left-right spectrum as traditionally understood. One finding based on that measure that seems relevant in context of the present study is the (widely discussed but itself controversial) asymmetric polarization thesis, which argues that "Polarization among U.S. legislators is asymmetric, as it has primarily been driven by a substantial rightward shift among congressional Republicans since the 1970s, alongside a much smaller leftward shift among congressional Democrats" (as summarized in the linked Wikipedia article). If, for example, higher polarization was associated with negative sentiments, this could be a potential explanation for Rozado's results. Again, this has to remain speculative, but it seems another notable omission in the paper's discussion of limitations.

    What does "bias" mean here?

    A fundamental problem of this study, which, to be fair, it shares with much fairness and bias research (in particular on Wikipedia's gender gap, where many studies similarly focus on binary comparisons that are likely to successfully appeal to an intuitive sense of fairness) consists of justifying its answers to the following two basic questions:

    1. What would be a perfectly fair baseline, a result that makes us confident to call Wikipedia unbiased?
    2. If there are deviations from that baseline (often labeled disparities, gaps or biases), what are the reasons for that – can we confidently assume they were caused by Wikipedia itself (e.g. demographic imbalances in Wikipedia's editorship), or are they more plausibly attributed to external factors?

    Regarding 1 (defining a baseline of unbiasedness), Rozado simply assumes that this should imply statistically indistinguishable levels of average sentiment between left and right-leaning terms. However, as cautioned by one leading scholar on quantitative measures of bias, "the 'one true fairness definition' is a wild goose chase" – there are often multiple different definitions available that can all be justified on ethical grounds, and are often contradictory. Above, we already alluded to two potentially diverging notions of political unbiasedness for Wikipedia (using an international instead of US metric for left vs right leaning, and taking into account polarization levels for politicians).

    But yet another question, highly relevant for Wikipedians interested in addressing the potential problems reported in this paper, is how much its definition lines up with Wikipedia's own definition of neutrality. Rozado clearly thinks that it does:

    Wikipedia’s neutral point of view (NPOV) policy aims for articles in Wikipedia to be written in an impartial and unbiased tone. Our results suggest that Wikipedia’s NPOV policy is not achieving its stated goal of political-viewpoint neutrality in Wikipedia articles.

    WP:NPOV indeed calls for avoiding subjective language and expressing judgments and opinions in Wikipedia's own voice, and Rozado's findings about the presence of non-neutral sentiments and emotions in Wikipedia articles are of some concern in that regard. However, that is not the core definition of NPOV. Rather, it refers to "representing fairly, proportionately, and, as far as possible, without editorial bias, all the significant views that have been published by reliable sources on a topic." What if the coverage of the terms examined by Rozado (politicians, etc.) in those reliable sources, in their aggregate, were also biased in the sense of Rozado's definition? US progressives might be inclined to invoke the snarky dictum "reality has a liberal bias" by comedian Stephen Colbert. Of course, conservatives might object that Wikipedia's definition of reliable sources (having "a reputation for fact-checking and accuracy") is itself biased, or applied in a biased way by Wikipedians. For some of these conservatives (at least those that are not also conservative feminists) it may be instructive to compare examinations of Wikipedia's gender gaps, which frequently focus on specific groups of notable people like in Rozado's study. And like him, they often implicitly assume a baseline of unbiasedness that implies perfect symmetry in Wikipedia's coverage – i.e. the absence of gaps or disparities. Wikipedians often object that this is in tension with the aforementioned requirement to reflect coverage in reliable sources. For example, Wikipedia's list of Fields medalists (the "Nobel prize of Mathematics") is 97% male – not because of Wikipedia editors' biases against women, but because of a severe gender imbalance in the field of mathematics that is only changing slowly, i.e. factors outside Wikipedia's influence.

    All this brings us to question 2. above (causality). While Rozado uses carefully couched language in this regard ("suggests" etc, e.g. "These trends constitute suggestive evidence of political bias embedded in Wikipedia articles"), such qualifications are unsurprisingly absent in much of the media coverage of this study (see also this issue's In the media). For example, the conservative magazine The American Spectator titled its article about the paper "Now We've Got Proof that Wikipedia is Biased."

    Commendably, the paper is accompanied by a published dataset, consisting of the analyzed Wikipedia text snippets together with the mentioned term and the sentiment or emotion identified by the automated annotation. For illustration, below are the sentiment ratings for mentions of the Yankee Institute for Public Policy (the last term in the dataset, as a non-cherry-picked example), with the term bolded:

    Dataset excerpt: Wikipedia paragraphs with sentiment for "Yankee Institute for Public Policy"
    positive "Carol Platt Liebau is president of the Yankee Institute for Public Policy.Liebau named new president of Yankee Institute She is also an attorney, political analyst, and conservative commentator. Her book Prude: How the Sex-Obsessed Culture Damages Girls (and America, Too!) was published in 2007."
    neutral "Affiliates

    Regular members are described as ""full-service think tanks"" operating independently within their respective states.

    Alabama: Alabama Policy Institute
    Alaska: Alaska Policy Forum
    [...]
    Connecticut: Yankee Institute for Public Policy
    [...]
    Wisconsin: MacIver Institute for Public Policy, Badger Institute, Wisconsin Institute for Law and Liberty, Institute for Reforming Government
    Wyoming: Wyoming Liberty Group"
    positive "The Yankee Institute for Public Policy is a free market, limited government American think tank based in Hartford, Connecticut, that researches Connecticut public policy questions. Organized as a 501(c)(3), the group's stated mission is to ""develop and advocate for free market, limited government public policy solutions in Connecticut."" Yankee was founded in 1984 by Bernard Zimmern, a French entrepreneur who was living in Norwalk, Connecticut, and Professor Gerald Gunderson of Trinity College. The organization is a member of the State Policy Network."
    neutral "He is formerly Chairman of the Yankee Institute for Public Policy. On November 3, 2015, he was elected First Selectman in his hometown of Stonington, Connecticut, which he once represented in Congress. He defeated the incumbent, George Crouse. Simmons did not seek reelection in 2019."
    negative "In Connecticut the union is closely identified with liberal Democratic politicians such as Governor Dannel Malloy and has clashed frequently with fiscally conservative Republicans such as former Governor John G. Rowland as well as the Yankee Institute for Public Policy, a free-market think tank."
    positive "In 2021, after leaving elective office, she was named a Board Director of several organizations. One is the Center for Workforce Inclusion, a national nonprofit in Washington, DC, that works to provide meaningful employment opportunities for older individuals. Another is the William F. Buckley Program at Yale, which aims to promote intellectual diversity, expand political discourse on campus, and expose students to often-unvoiced views at Yale University. She also serves on the Board of the Helicon Foundation, which explores chamber music in its historical context by presenting and producing period performances, including an annual subscription series of four Symposiums in New York featuring both performance and discussion of chamber music. She is also a Board Director of the American Hospital of Paris Foundation, which provides funding support for the operations of the American Hospital of Paris and functions as the link between the Hospital and the United States, funding many collaborative and exchange programs with New York-Presbyterian Hospital. She is also a Fellow of the Yankee Institute for Public Policy, a research and citizen education organization that focuses on free markets and limited government, as well as issues of transparency and good governance."
    positive "He was later elected chairman of the New Hampshire Republican State Committee, a position he held from 2007 to 2008. When he was elected he was 34 years old, making him the youngest state party chairman in the history of the United States at the time. His term as chairman included the 2008 New Hampshire primary, the first primary in the 2008 United States presidential election. He later served as the executive director of the Yankee Institute for Public Policy for five years, beginning in 2009. He is the author of a book about the New Hampshire primary, entitled Granite Steps, and the founder of the immigration reform advocacy group Americans By Choice."

    Briefly


    Other recent publications

    Other recent publications that could not be covered in time for this issue include the items listed below. Contributions, whether reviewing or summarizing newly published research, are always welcome.

    How English Wikipedia mediates East Asian historical disputes with Habermasian communicative rationality

    From the abstract: [3]

    "We compare the portrayals of Balhae, an ancient kingdom with contested contexts between [South Korea and China]. By comparing Chinese, Korean, and English Wikipedia entries on Balhae, we identify differences in narrative construction and framing. Employing Habermas’s typology of human action, we scrutinize related talk pages on English Wikipedia to examine the strategic actions multinational contributors employ to shape historical representation. This exploration reveals the dual role of online platforms in both amplifying and mediating historical disputes. While Wikipedia’s policies promote rational discourse, our findings indicate that contributors often vacillate between strategic and communicative actions. Nonetheless, the resulting article approximates Habermasian ideals of communicative rationality."

    From the paper:

    "The English Wikipedia presents Balhae as a multi-ethnic kingdom, refraining from emphasizing the dominance of a single tribe. In comparison to the two aforementioned excerpts [from Chinese and Korean Wikipedia], the lead section of the English Wikipedia concentrates more on factual aspects of history, thus excluding descriptions that might entail divergent interpretations. In other words, this account of Balhae has thus far proven acceptable to a majority of Wikipedians from diverse backgrounds. [...] Compared to other language versions, the English Wikipedia forthrightly acknowledges the potential disputes regarding Balhae's origin, ethnic makeup, and territorial boundaries, paving the way for an open and transparent exploration of these contested historical subjects. The separate 'Balhae controversies' entry is dedicated to unpacking the contentious issues. In essence, the English article adopts a more encyclopedic tone, aligning closely with Wikipedia's mission of providing information without imposing a certain perspective."

    (See also excerpts)

    Facebook/Meta's "No Language Left Behind" translation model used on Wikipedia

    From the abstract of this publication by a large group of researchers (most of them affiliated with Meta AI):[4]

    "Focusing on improving the translation qualities of a relatively small group of high-resource languages comes at the expense of directing research attention to low-resource languages, exacerbating digital inequities in the long run. To break this pattern, here we introduce No Language Left Behind—a single massively multilingual model that leverages transfer learning across languages. [...] Compared with the previous state-of-the-art models, our model achieves an average of 44% improvement in translation quality as measured by BLEU. By demonstrating how to scale NMT [neural machine translation] to 200 languages and making all contributions in this effort freely available for non-commercial use, our work lays important groundwork for the development of a universal translation system."

    "Four months after the launch of NLLB-200 [in 2022], Wikimedia reported that our model was the third most used machine translation engine used by Wikipedia editors (accounting for 3.8% of all published translations) (https://web.archive.org/web/20221107181300/https://nbviewer.org/github/wikimedia-research/machine-translation-service-analysis-2022/blob/main/mt_service_comparison_Sept2022_update.ipynb). Compared with other machine translation services and across all languages, articles translated with NLLB-200 has the lowest percentage of deletion (0.13%) and highest percentage of translation modification kept under 10%."

    "Which Nigerian-Pidgin does Generative AI speak?" – only the BBC's, not Wikipedia's

    From the abstract:[5]

    "Naija is the Nigerian-Pidgin spoken by approx. 120M speakers in Nigeria [...]. Although it has mainly been a spoken language until recently, there are currently two written genres (BBC and Wikipedia) in Naija. Through statistical analyses and Machine Translation experiments, we prove that these two genres do not represent each other (i.e., there are linguistic differences in word order and vocabulary) and Generative AI operates only based on Naija written in the BBC genre. In other words, Naija written in Wikipedia genre is not represented in Generative AI."

    The paper's findings are consistent with an analysis by the Wikimedia Foundation's research department that compared the number of Wikipedia articles to the number of speakers for the top 20 most-spoken languages, where Naija stood out as one of the most underrepresented.

    "[A] surprising tension between Wikipedia's principle of safeguarding against self-promotion and the scholarly norm of 'due credit'"

    From the abstract:[6]

    Although Wikipedia offers guidelines for determining when a scientist qualifies for their own article, it currently lacks guidance regarding whether a scientist should be acknowledged in articles related to the innovation processes to which they have contributed. To explore how Wikipedia addresses this issue of scientific "micro-notability", we introduce a digital method called Name Edit Analysis, enabling us to quantitatively and qualitatively trace mentions of scientists within Wikipedia's articles. We study two CRISPR-related Wikipedia articles and find dynamic negotiations of micro-notability as well as a surprising tension between Wikipedia’s principle of safeguarding against self-promotion and the scholarly norm of “due credit.” To reconcile this tension, we propose that Wikipedians and scientists collaborate to establish specific micro-notability guidelines that acknowledge scientific contributions while preventing excessive self-promotion.

    See also coverage of a different paper that likewise analyzed Wikipedia's coverage of CRISPR: "Wikipedia as a tool for contemporary history of science: A case study on CRISPR"

    "How article category in Wikipedia determines the heterogeneity of its editors"

    From the abstract:[7]

    " [...] the quality of Wikipedia articles rises with the number of editors per article as well as a greater diversity among them. Here, we address a not yet documented potential threat to those preconditions: self-selection of Wikipedia editors to articles. Specifically, we expected articles with a clear-cut link to a specific country (e.g., about its highest mountain, "national" article category) to attract a larger proportion of editors of that nationality when compared to articles without any specific link to that country (e.g., "gravity", "universal" article category), whereas articles with a link to several countries (e.g., "United Nations", "international" article category) should fall in between. Across several language versions, hundreds of different articles, and hundreds of thousands of editors, we find the expected effect [...]"

    "What do they make us see:" The "cultural bias" of GLAMs is worse on Wikidata

    From the abstract:[8]

    "Large cultural heritage datasets from museum collections tend to be biased and demonstrate omissions that result from a series of decisions at various stages of the collection construction. The purpose of this study is to apply a set of ethical criteria to compare the level of bias of six online databases produced by two major art museums, identifying the most biased and the least biased databases. [...] For most variables the online system database is more balanced and ethical than the API dataset and Wikidata item collection of the two museums."

    References

    1. ^ Rozado, David (June 2024). "Is Wikipedia Politically Biased?". Manhattan Institute. Dataset: https://doi.org/10.5281/zenodo.10775984
    2. ^ Kerkhof, Anna; Münster, Johannes (2019-10-02). "Detecting coverage bias in user-generated content". Journal of Media Economics. 32 (3–4): 99–130. doi:10.1080/08997764.2021.1903168. ISSN 0899-7764.
    3. ^ Jee, Jonghyun; Kim, Byungjun; Jun, Bong Gwan (2024). "The role of English Wikipedia in mediating East Asian historical disputes: the case of Balhae". Asian Journal of Communication: 1–20. doi:10.1080/01292986.2024.2342822. ISSN 0129-2986. Closed access icon (access for Wikipedia Library users)
    4. ^ Costa-jussà, Marta R.; Cross, James; Çelebi, Onur; Elbayad, Maha; Heafield, Kenneth; Heffernan, Kevin; Kalbassi, Elahe; Lam, Janice; Licht, Daniel; Maillard, Jean; Sun, Anna; Wang, Skyler; Wenzek, Guillaume; Youngblood, Al; Akula, Bapi; Barrault, Loic; Gonzalez, Gabriel Mejia; Hansanti, Prangthip; Hoffman, John; Jarrett, Semarley; Sadagopan, Kaushik Ram; Rowe, Dirk; Spruit, Shannon; Tran, Chau; Andrews, Pierre; Ayan, Necip Fazil; Bhosale, Shruti; Edunov, Sergey; Fan, Angela; Gao, Cynthia; Goswami, Vedanuj; Guzmán, Francisco; Koehn, Philipp; Mourachko, Alexandre; Ropers, Christophe; Saleem, Safiyyah; Schwenk, Holger; Wang, Jeff; NLLB Team (June 2024). "Scaling neural machine translation to 200 languages". Nature. 630 (8018): 841–846. Bibcode:2024Natur.630..841N. doi:10.1038/s41586-024-07335-x. ISSN 1476-4687. PMC 11208141. PMID 38839963.
    5. ^ Adelani, David Ifeoluwa; Doğruöz, A. Seza; Shode, Iyanuoluwa; Aremu, Anuoluwapo (2024-04-30). "Which Nigerian-Pidgin does Generative AI speak?: Issues about Representativeness and Bias for Multilingual and Low Resource Languages". arXiv:2404.19442 [cs.CL].
    6. ^ Simons, Arno; Kircheis, Wolfgang; Schmidt, Marion; Potthast, Martin; Stein, Benno (2024-02-28). "Who are the "Heroes of CRISPR"? Public science communication on Wikipedia and the challenge of micro-notability". Public Understanding of Science. doi:10.1177/09636625241229923. ISSN 0963-6625. PMID 38419208. blog post
    7. ^ Oeberst, Aileen; Ridderbecks, Till (2024-01-07). "How article category in Wikipedia determines the heterogeneity of its editors". Scientific Reports. 14 (1): 740. Bibcode:2024NatSR..14..740O. doi:10.1038/s41598-023-50448-y. ISSN 2045-2322. PMC 10772120. PMID 38185716.
    8. ^ Zhitomirsky-Geffet, Maayan; Kizhner, Inna; Minster, Sara (2022-01-01). "What do they make us see: a comparative study of cultural bias in online databases of two large museums". Journal of Documentation. 79 (2): 320–340. doi:10.1108/JD-02-2022-0047. ISSN 0022-0418. Closed access icon / freely accessible version


    ToDo List

    Miscellaneous tasks

    Categories to look through

    (See also this much larger list of relevant articles without a lead image)

    Translation ToDo

    A list of related articles particularly good and notable enough to be worthy of a solid translation effort

    Requested articles (in general)

    1. ^ Backman, J. (2022). Radical conservatism and the Heideggerian right : Heidegger, de Benoist, Dugin. Frontiers in Political Science, 4, Article 941799. https://doi.org/10.3389/fpos.2022.941799

    Merging ToDo

    A list of related articles that may have resulted from a WP:POVFORK or may, at least, look like the functional equivalents of one
    Note that the exact target of a potential merge must not be provided here and that multiple options (e.g. generous use of Template:Excerpt) might accomplish the same