Media representation of migrant crime: Hypotheticals, prominence, and migration pros and cons in select western newspaper coverage
Bibliographic record
Abstract
This content analysis examines newspaper representation of migrant criminality in Canada, the UK, and the US. Existing studies demonstrate a dynamic relationship between media coverage, perceptions of migration, and politics/lawmaking, as well as the media’s role in maintaining the gap between empirical knowledge and common understanding of migrant crime. Logistic and OLS regression are employed to evaluate (1) the hypothetical discussion of migrant crime (speculative/risk-oriented content as opposed to the discussion of a real crime event), and (2) article prominence in the form of word count. Qualitative thematic analyses are used to explore the nature of (3) pro-migrant content, such as economic benefits, and (4) anti-migrant content, such as threats to values and resources. Results are considered in the contexts of rising populism, media influence and accountability, promotion of stereotypes and public concern, and the perceived risks of migration and subsequent effects on human and civil rights.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".