L'Enquête nationale sur les femmes et les filles autochtones disparues et assassinées au Canada: Explorer la relation entre l'existence de critiques externes et la prise de parole des témoins lors des audiences communautaires
Bibliographic record
Abstract
Faced with the alarming rates of disappearances and murders of Indigenous women, girls and 2SLGBTQQIA+ people in Canada and in response to the demands of victims' families and Indigenous women's associations, the Canadian government set up the National Inquiry into Missing and Murdered Indigenous Women and Girls (2016-2019). Its mandate: to identify the systemic causes of violence and produce effective recommendations to remedy them. From its announcement and during the course of its work, the inquiry faced a great deal of criticism, particularly from families and Indigenous women's associations, undermining the trust of many in the commissioners and in the process. It was thus against the backdrop of those brewing tensions that many people affected by the violence came forward to tell their stories at community hearings held across the country. As we consider public testimony to be a vector of social agency for these witnesses, we ask how external critiques conveyed in the media sphere influenced these narrative spaces internal to the inquiry. Through the use of computer-assisted text analysis (based in textometry) applied on a corpus of transcripts from the fifteen community hearings, we were able to identify the presence of certain criticisms, which occupied a relatively small space in the hearings. What's more, our explorations enabled us to reveal that witnesses bore a dual responsibility: to tell their story and to avoid downgrading the investigation in progress.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.010 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".