Bibliographic record
Abstract
About three years ago, a colleague suggested I write a new book on violence against women in Canada.I questioned the need for another book on the abuse of women in private places and in intimate relationships.After all, since the late 1970s, many books on various types of male-to-female violence have been published.However, since Canadian scholars, practitioners, and activists are constantly generating new empirical, theoretical, and political ways of understanding sexual assaults, wife beating, femicide, and the like, I decided it was indeed time to write an overview of their work.Violence Against Women: Myths, Facts, Controversies also includes some important contributions made by authors in other countries, especially those based in the United States, the United Kingdom, and Australia.Chapter One describes and evaluates narrow and broad definitions of violence against women.Certainly, defining male-to-female violence is subject to much debate.Chapter Two focuses on the amount of violence Canadian women experience in current or former intimate relationships.A key point made there is that acts of violence are not rare incidents and in fact can happen to any woman.Further, contrary to popular belief, women are much safer on the streets than they are behind closed doors.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.402 | 0.208 |
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".