Bibliographic record
Abstract
The issue of honour killings has become a prominent topic of discussion in the Western discourse of violence against immigrant women. In Canada, particularly, the recent high-profile cases of honour killings have drawn increased attention from the media, academics and the public. The prevalent discussion links these murders to the broader issues of immigration, multiculturalism, and violence against immigrant women. In this thesis, I examine the nature of honour killings, their components, and the discourse of honour killings in its Canadian context. In doing so, I conduct a textual analysis of the representation of three recent honour killings in two major Canadian newspapers; The Toronto Star and The Globe and Mail. Results suggest that honour killings touched a nerve in Canadian media leading to the use of culturalist approaches to understand and represent these killings. This culturalist approach to the debate created serious obstacles for clarifying or explaining this form of violence against women. It further hindered any constructive public debate about ending these killings. The consequences of the culturalist approach to honour killings as well as recommendations for future research and theoretical developments in this area of violence against women are suggested.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.020 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".