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Record W3203634126

Making Change on Gender-Based Violence: Assessing Shifting Political Opportunities in Canada

2021· article· en· W3203634126 on OpenAlexaboutno aff
Lisa M. Boucher

Bibliographic record

VenueJournal of international women's studies · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicGender Politics and Representation
Canadian institutionsnot available
Fundersnot available
KeywordsPoliticsPolitical sciencePolitical changePolitical violenceGender studiesCriminologySociologyLaw
DOInot available

Abstract

fetched live from OpenAlex

Feminist anti-violence organizations provide much needed services and advocate for changes to culture and policy. However, their ability to continue with this work is jeopardized by fraught and changing relations with governments. This is especially the case for state-funded feminist service organizations which, through their ties to state funders, risk their ability to engage in advocacy. While scholars and activists warn of the challenges associated with state funding, situating funding relationships within particular social, historical, geographic, and political contexts can illuminate both the threats facing feminist service organizations, as well as openings in the political opportunity structure. Using the Canadian province of Ontario as a case study, this paper highlights changes to funding for anti-violence work between 1990 and 2015 and considers the implications of shifts in the funding regime. My findings indicate that while state resources for anti-violence initiatives have expanded over time in both the province of Ontario and at the federal level, neoliberal governance has altered the distribution of government funding which has contributed to heightened competition between organizations. I conclude by offering reflections on existing political opportunities for the feminist anti-violence movement 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 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)0.001
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 score0.837
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.333
GPT teacher head0.441
Teacher spread0.108 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

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