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Record W2960467577 · doi:10.3167/ghs.2019.120201

Mobilizing a Social Justice Agenda

2019· article· en· W2960467577 on OpenAlexaffabout
Claudia Mitchell

Bibliographic record

VenueGirlhood Studies · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Security, and Conflict
Canadian institutionsMcGill University
Fundersnot available
KeywordsSummitScholarshipGenocidePolitical scienceIndigenousSocial justiceEconomic JusticeSociologyPower (physics)Gender studiesCriminologyLawGeography

Abstract

fetched live from OpenAlex

As this issue of Girlhood Studies went to press, two very dramatic moments in the history of girls and young women were in the public eye. One was the large 8000-strong gathering of NGOs, researchers, politicians, and activists from 165 countries at the Women Deliver Global Summit on gender equality that took place in Vancouver, Canada, from 3 to 6 June 2019. There, according the program, the focus was on how power can both hinder and drive progress and change for a world that is more gender equal. On 3 June, the long-awaited report of the National Inquiry into Missing and Murdered Indigenous Women and Girls (MMIWG) in Canada was released, with its 231 recommendations or calls for social justice to address what is now acknowledged as being part of what was (and continues to be) cultural genocide. Both the Global Summit and the report on MMIWG are reminders of the need for the blend of scholarship and activism that is so critical to advancing issues of equity and to implementing recommendations to achieve this. This unthemed issue with its broad range of geographic locations, concerns, and methods and its attention to activism, along with scholarship that features work from both the humanities and social sciences, is key in relation to mobilizing a social justice agenda.

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)0.000
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.390
Threshold uncertainty score0.841

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.114
GPT teacher head0.394
Teacher spread0.280 · 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
Published2019
Admission routes2
Has abstractyes

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