Review of: Totten, M.D. (2001). Guys, Gangs and Girlfriend Abuse. Toronto: University of Toronto Press.
Bibliographic record
Abstract
MarkTotten's book, Guys, Gangs and Girlfriend Abuse, presents Totten's own research on young males' abusive behaviour towards girlfriends within the existing body of literature.Totten presents a well-rounded and informative account of abusive male behaviour.He employs a sociological approach that builds upon other theoretical works and provides new insights as to how young men understand and rationalize their abusive behaviours towards women.Totten uses qualitative research methods to conduct in-depth interviews with thirty young males in order to understand abuse through the eyes of the perpetrators.He categorizes and analyzes his participants' responses and compares and contrasts their understanding of their abuse.Totten explains how abuse against girlfriends is contingent upon several reoccurring themes seen throughout his research.His book illustrates the impact that familial and gender ideologies have on young marginalized males' view of masculinity.These rigid ideologies are established and maintained by families, male peer groups and gangs.The connections he makes between masculinity, family and peers/gangs shows the important role these themes serve in establishing, maintaining and rationalizing males physical, sexual and emotional abuse towards girlfriends and also towards gays and visible minorities.Totten approaches the issue of men's abuse against women by employing an interpretative social-constructionist standpoint which draws upon different areas of sociological thought.Totten outlines the types of abuse that he looks at in his study, namely physical, sexual, and emotional abuse.After screening participants for social and economic marginalization and the
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.019 |
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".