When is a Myth Itself a Myth? Immigrant Criminality and the Canadian Public
Bibliographic record
Abstract
Survey-based evidence gathered over the past several decades suggests that substantial minorities of the Canadian public associate immigrants with crime and crime with immigrants. In this note, we ask whether the myth of immigrant criminality imputed to the public is not itself a myth. We question whether the connection is a salient and enduring part of the public’s mindset or whether it is largely an artifact of the closed-ended items employed to explore the topic. We argue that responses to closed-ended questions on this topic are affected by a “halo effect” response bias – a tendency to associated positive attributes with positively evaluated targets and negative attributes to negatively evaluated targets. In support, we show (1) that responses to open-ended questions tell a very different story, (2) that attitudes toward immigrants strongly predict the likelihood of making the immigrant-crime connection when closed-ended items are used, and (3) that priming a possible immigrant-criminal linkage in a survey enhances this likelihood for subsequent items.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".