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Record W4379779903 · doi:10.1353/leg.2020.0028

Suffrage Elimination Dance

2020· article· en· W4379779903 on OpenAlexaboutno aff
Mary Chapman

Bibliographic record

VenueLegacy A Journal of American Women Writers · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicRace, History, and American Society
Canadian institutionsnot available
Fundersnot available
KeywordsTurnoutDancePopulationPresidential systemPolitical scienceSuffrageVotingLawGender studiesPoliticsSociologyDemographyVisual artsArt

Abstract

fetched live from OpenAlex

Suffrage Elimination Dance Mary Chapman I hope that by celebrating the ratification of the Nineteenth Amendment, we can convince all voters of the importance of not taking this right for granted. Voter turnout in the United States is among the lowest in the free world. Less than 56 percent of the voting-age population voted in the 2016 presidential election, and while that turnout represented a slight improvement over the turnout for 2012, it was a worse turnout than for the 2008 election. We should be very worried about what low turnout could mean for the presidential election this fall. When I teach classes about the suffrage movement in my classrooms, some students “zone out.” Their resigned faces seem to say: “Who cares?” “Suffrage is a minor movement.” “Big deal.” So I play a game with them, based on a 1950s party game called the “Elimination Dance.” In an “Elimination Dance,” a dance caller would announce certain criteria and the dancers who met those criteria would have to sit down: “All those wearing blue socks? Sit down!” “All those whose names begin with A? Sit down!” The dancers left standing at the end are the “winners.” I ask students who are willing to play the game to stand up. Then, I call out the groups that were not eligible to vote a century ago in my jurisdiction and ask students belonging to those groups to sit down: in British Columbia, Canada, these include women, Indigenous/First Nations/Native Canadians, people of Asian ancestry, and many, many more. I don’t have to call out all the criteria that applied one hundred years ago—for example, people under twenty-five and people without $500 worth of property—because after I’ve asked students belonging to the first three or four groups to sit down, in a class of 150 students, there might only be four or five left standing. These people would be the class’s only voters if we lived before women agitated to expand the franchise. And in part because of the precedent of suffragists’ efforts to enfranchise women, other disenfranchised groups were gradually recognized over the next few decades. When I played this game during the 2015 Canadian Federal election, students were astounded and followed me in droves when I announced that I was heading to a poll station set up on campus, where students living away from home could fill out ballots for whichever constituency they were eligible to vote in—an experimental initiative by Elections Canada. Try the Elimination Dance in your classrooms on Election Day! [End Page 302] Mary Chapman University of British Columbia Copyright © 2021 University of Nebraska Press

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.624
Threshold uncertainty score0.496

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.278
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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