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Record W3020509087 · doi:10.1163/9789004377714

Disrupting Shameful Legacies

2018· book· en· W3020509087 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typebook
Languageen
FieldSocial Sciences
TopicGender, Security, and Conflict
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousGender studiesColonialismContext (archaeology)Sexual violenceLegislationSociologyPolitical scienceCriminologyHistoryLaw

Abstract

fetched live from OpenAlex

Much has been written in Canada and South Africa about sexual violence in the context of colonial legacies, particularly for Indigenous girls and young women. While both countries have attempted to deal with the past through Truth and Reconciliation Commissions and Canada has embarked upon its National Inquiry on Missing and Murdered Indigenous Women and Girls, there remains a great deal left to do. Across the two countries, history, legislation and the lived experiences of young people, and especially girls and young women point to a deeply rooted situation of marginalization. Violence on girls’ and women’s bodies also reflects violence on the land and especially issues of dispossession. What approaches and methods would make it possible for girls and young women, as knowers and actors, especially those who are the most marginalized, to influence social policy and social change in the context of sexual violence? Taken as a whole, the chapters in Disrupting Shameful Legacies: Girls and Young Women Speaking Back through the Arts to Address Sexual Violence which come out of a transnational study on sexual violence suggest a new legacy, one that is based on methodologies that seek to disrupt colonial legacies, by privileging speaking up and speaking back through the arts and visual practice to challenge the situation of sexual violence. At the same time, the fact that so many of the authors of the various chapters are themselves Indigenous young people from either Canada or South Africa also suggests a new legacy of leadership for change.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.538
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.002

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 head0.048
GPT teacher head0.329
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; both teacher heads agree on what is shown here.

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

Citations12
Published2018
Admission routes1
Has abstractyes

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