Missing and Murdered Indigenous Women, Girls, and Two-Spirit Trends in Canada
Bibliographic record
Abstract
In North America, Indigenous women, girls, and Two-Spirit (IWG2) are at an increased risk of victimization. The matter long predates the present-day movement for a resolution. As a result, there is a severe data deficit regarding Missing and Murdered Indigenous Women, Girls, and Two-Spirit (MMIWG2) in Canada. On top of existing struggles within Indigenous communities, there is fear that they or a loved one will go missing at any moment and receive little to no aid in their recovery. Without adequate information, legislators cannot address the situation at hand. The MMIWG2 database provides this necessary information. The MMIWG2 research consisted of two phases. The first phase involved gathering over 1000 cases of MMIWG2 in Canada from media publications, government resources, and community databases. The second phase consisted of verifying the authenticity of each missing person's case and collecting corresponding information. This information included victim and offender demographics, time between an individual being reported missing and law enforcement responses, and if the case received follow-up or resolution. The verified information has allowed us to understand the effectiveness of Canada’s response to the issue, the overarching issue of MMIWG2, as well as allowed us to identify the dominant features of these cases.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".