Missing and Murdered Indigenous Women, Girls, and Two-Spirit (MMIWG2)
Bibliographic record
Abstract
The Issue of Missing and Murdered Indigenous Women, Girls, and Two-Spirit (MMIWG2) is a pressing matter within the United States (U.S.) and Canada. The MMIWG2 database consists of around 4,000 individual MMIWG2 cases within both countries. It is developed from community-led and government databases and sources from the U.S. and Canada. The MMIWG2 research confirms the existence of these cases through reliable news sources, thesis's, government documents, etc. Facts of each case are broken down (victim demographics, offender demographics, date of incident, date of resolution (body was found), date of report (when the incident was reported to law enforcement), and more). Upon completion, the data set can be used for statistical analysis, public knowledge, law enforcement, etc. This is, however, an ongoing project. Thus, this summer, only about one third of the MMIWG2 data set will be coded (making two-thirds total because coders last year finished about one third of the set as well). Only about 600 cases have been coded thus far, for both the U.S. and Canada (half U.S. and half Canada). This section only views the Canadian side of the data set. Because it is not finished, no conclusions can be made from the data set. However, natural trends can be graphed and observed. Examples given in this presentation will be the geographical trends of the MMIWG2 cases, months in which MMIWG2 incidents occur, and the MMIWG2 case status percentages.
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 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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".