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Record W4403777305 · doi:10.12797/9788383681696.01

The Quest for Gender Equality in Canada: Introduction

2024· book-chapter· en· W4403777305 on OpenAlexaboutno aff
Gabriela Kwiatek, Tomasz Soroka

Bibliographic record

VenueKsiegarnia Akademicka Publishing eBooks · 2024
Typebook-chapter
Languageen
FieldSocial Sciences
TopicGender Politics and Representation
Canadian institutionsnot available
Fundersnot available
KeywordsGender equalityPolitical scienceGender studiesSociology

Abstract

fetched live from OpenAlex

The collective publication, titled Canada: A Model for Gender Equality?, highlights the work of emerging scholars as the project is conducted by the Students’ Association for American Studies. The Students’ Association prioritizes the promotion of Canadian cultures in Poland among its primary objectives. This interdisciplinary publication comprises 15 chapters, addressing various issues with a primary focus on feminism and women’s rights in Canada. It analyzes the notion of gender equality through an examination of governmental programs, literary works, and social initiatives. It also discusses the challenges faced by marginalized groups, including women of Indigenous descent, immigrant women, and 2SLGBTQIA+ individuals. The book highlights legislative progress and the necessity to continue striving for comprehensive gender equality in social, political, and economic spheres. The historical context of gender equality initiatives in Canada was examined, highlighting the contributions of Canada’s feminist movements that facilitated women’s advancement in male-dominated arenas. The book underscores the need for further reforms to tackle the pervasive systemic barriers and discrimination that persistently affect the lives of many women and individuals of diverse gender identities in Canada.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.086
Threshold uncertainty score0.624

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.006
Science and technology studies0.0130.006
Scholarly communication0.0110.002
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0150.002

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 head0.060
GPT teacher head0.303
Teacher spread0.243 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2024
Admission routes1
Has abstractyes

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