“…she hit me and we stopped, she was yelling and screaming at me”: An exploration of the perceptions, identities and stigma of men and domestic violence
Bibliographic record
Abstract
Domestic violence is a prevalent and important global social issue. This thesis is unique as it seeks to mobilize knowledge of male perceptions and experiences of domestic violence by giving voice to a rather silenced, invisible, and often neglected group of individuals. Through face-to-face conversations with nine men with ties to a community organization located within the Greater Toronto Area (GTA), a qualitative conversation and thematic analysis was conducted. Through this interpretivist framework, I closely examine their lived experiences and perceptions of social reality as these men navigate the labels of masculine identities, stigma and social construction of domestic violence. Their perceptions inform us of their experiences with the criminal justice system and access to service(s). Core themes that emerged from the data are: (a) Masculine Identities, (b) Stigma Management, and (c) Barriers to Service. Practical implications, recommendations, future research and limitations in dealing with this neglected population is discussed.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.015 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".