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Record W7010569788

'Is Canada's Prime Minister merely a Facebook feminist': Evaluating the gender sensitivity of Canada's Parliament

2017· other· en· W7010569788 on OpenAlexaboutno aff

Bibliographic record

VenueArca (British Columbia Electronic Library Network) · 2017
Typeother
Languageen
FieldMedicine
TopicBiomedical and Chemical Research
Canadian institutionsnot available
Fundersnot available
KeywordsNucleofectionGovernment (linguistics)GloomDemotionSubpoenaPretext
DOInot available

Abstract

fetched live from OpenAlex

Presented at the <a href="http://www.ecpg.eu/2017-conference.html">ECPG (European Conference on Politics and Gender)</a> in Lausanne, Switzerland (June, 2017). \n\nCanada ranks 64 of 193 countries (IPU, 2016) with women making up 26 percent of its members of parliament. Upon winning the 2015 general election Canada’s new Liberal prime minister Justin Trudeau made international headlines by appointing the nation’s first federal sex-balanced cabinet; when asked ‘why’ he replied ‘because it’s 2015’, which went on to become the widely trending #BecauseIts2015 and Facebook meme. Trudeau soon declared himself a feminist and his as the party for feminists. Yet Canada’s parliament may not be what it appears. For example, Trudeau’s feminist government recently rejected a private members’ bill designed to incentivize parties to run more women candidates. The media and activists have referred to the bill as the first piece of feminist legislation to be introduced in the current Parliament. Trudeau missed the vote and the Status of Woman minister along with 78 percent of the Liberal caucus voted to defeat Bill C-237 when it came forward after second reading. After a year in office, the Trudeau government continues to describe itself as feminist, but has thus far done little to advance policies that would lead to gender equity. This paper uses interviews with Canadian MPs to explore the concept “gender sensitive parliaments” using Canada as an example of the tension between rhetoric, symbolism, and legislative action.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.341
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.1180.000

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 head0.023
GPT teacher head0.266
Teacher spread0.244 · 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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2017
Admission routes1
Has abstractyes

Explore more

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