Media discourse : a compare and contrast of language used to portray female offenders in Canada and China
Bibliographic record
Abstract
Many studies were done to reveal that individual's perception towards certain group of people is established and influenced by outside sources.Female is considered the vulnerable group in the society and their presence in media is usually sensitive, especially with the case of female offenders.Their actions are distorted and exaggerated, and a wrong image of female offenders is delivered to the public.This phenomenon is not exclusive to one country, but prevails in many other countries and cross different cultures.This paper aims to compare and contrast how female offenders are portrayed by media in Canada and China.All 44 articles collected from Toronto Stars, Beijing Legal Times, and Wang Yi were categorized and analyzed through the use of content analysis, seven themes were created to help the researcher conclude and analyze the patterns in regard to female offender 's misrepresentation in media.The results have showed that while Canadian media is less likely to objectify female offenders than its Chinese counterpart, both countries' media tend to adhere to patriarchal values when depicting them.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".