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Record W7132989733

No Escape: The Weaponization of Gender for the Purposes of Digital Transnational Repression

2024· other· en· W7132989733 on OpenAlexfundaboutno aff
Noura Aljizawi, Siena Anstis, Marcus Michaelsen, Veronica Arroyo, Shaila Baran, Maria Bikbulatova, Gözde Böcü, Camila Franco, Arzu Geybulla, Muetter Iliqud, Nicola Lawford, Émilie LaFlèche, Gabrielle Lim, Levi Meletti, Maryam Mirza, Zoe Panday, Claire Posno, Zoë Reichert, Berhan Kefyalew Taye, Angela Yang

Bibliographic record

VenueTSpace · 2024
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
FundersUniversity of Toronto
KeywordsDigital rightsPrincipal (computer security)Human rightsResearch ethicsCourageProtocol (science)
DOInot available

Abstract

fetched live from OpenAlex

This project was supervised by Professor Ronald Deibert, principal investigator, under the University of Toronto Research Ethics Protocol # 42719, “The Gender-Dimensions of Digital Transnational Repression.” We acknowledge and thank all research participants who generously participated in interviews for this report and shared their personal experiences with us. Their stories demonstrate the courage required to engage in human rights activism and we are grateful for their willingness to speak with us.

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)0.008
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.032
Scholarly communication0.0100.009
Open science0.0010.012
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0110.001

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 head0.037
GPT teacher head0.337
Teacher spread0.300 · 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 designTheoretical or conceptual
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
Published2024
Admission routes2
Has abstractyes

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