Bibliographic record
Abstract
In Canada, a woman is killed by her intimate partner every six days. Alberta has one of the highest rates of domestic violence in the country. Starting in the 1970s, Alberta women’s shelters have assisted women in crisis. Much more than a safe place to sleep, shelters work to prevent violence through education and training, connect people and communities, and support the complex needs of survivors through a multitude of services. We Need to Do This is the story of Alberta women's shelters. Based on dozens of in-depth interviews, it traces the evolution of a progressive social movement in a traditionally conservative province. These are the stories of women whose voices may otherwise never have been heard: entry-level workers at fledgling shelters battling the assumption that their facilities would create crime, small-town shelter directors forced to self-censor or lose communityand financialsupport, Indigenous women fighting to serve their sisters in Indigenous spaces. Beginning with the women who founded the first shelters, and continuing through the establishment of the Alberta Council of Women's Shelters to the present day, We Need to Do This is a story of hope and survival for the women’s shelter movement and for the mothers, sisters, aunts, cousins, and daughters it continues to serve.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.029 | 0.016 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.059 | 0.030 |
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".