Gender-based violence (GBV) against immigrant women living in Canada : blending big data and critical discourse approaches to news media representations
Bibliographic record
Abstract
Discussions on the role of gender in migration have increased significantly in the last few years. This study explores the impact of gender-based violence (GBV) on immigrant women living in Canada as reported and constructed by news media. The study was carried out using Big Data, with the aid of several clustering and classifying artificial intelligence (AI) technologies (Machine Learning Tools). After a significant amount of news media data was gleaned through such technologies, critical discourse analysis (CDA) was used to examine how discourse, specifically from Canadian news media, reports and thus constructs GBV against immigrant women living in Canada. Through this critical lens, it was concluded that immigrant women were often viewed as passive victims and never as heroes / survivors / or agents in their lives; they were also presented as ‘other' in Canadian society by news media reflecting stereotypical images of them and exoticizing their GBV experiences. Such constructions, in turn, contributed to aggravating and \nperpetuating their GBV experiences as well as magnifying pre-existing inequalities.
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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 | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.026 | 0.013 |
| Scholarly communication | 0.017 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".