Canadians' Attitudes toward Immigrants who Claim Employment Discrimination
Bibliographic record
Abstract
This dissertation examined how prejudice may operate in the treatment of immigrants when they claim workplace discrimination. In line with the Justification-Suppression Model of the Expression of Prejudice (JSM; Crandall & Eshleman, 2003), I expected more negative attitudes toward an immigrant claimant from a dissimilar culture (Iran) compared to an immigrant claimant from a similar culture (Britain) and a second generation Iranian Canadian. All three studies utilized experimental design. The results of Study 1 demonstrated that the Iranian claimant was especially likely to be seen as not having experienced discrimination, more deserving of and responsible for the dismissal, and was especially likely to be derogated, compared to an Iranian Canadian claimant. Attributions of personal responsibility mediated the effect of country of origin on target derogation and outcome deservingness. In Study 2, the claimant from Iran elicited more negative attitudes than a claimant from Britain. Moreover, participants who suppressed their prejudice less reported more bias against the claimant from Iran as compared to the Iranian Canadian claimant. In addition to the country of origin, Study 3 manipulated a source of attributions for discrimination. To do so, in addition to situational ambiguity, Study 3 added two other explanations for the discrimination claimant’s contract termination – one considered to be internal to the claimant and the other external. The results demonstrated suppression of prejudice against the claimant from Iran in all conditions. When the claimant was clearly unskilled for the job, there was an evidence of a “black sheep effect”- more negative attitudes toward the British claimant. Finally, in the situation with an authority figure responsible for discrimination, the second generation Iranian Canadian elicited more negativity. These effects may be explained by attributions of personal responsibility and judgements of outcome deservingness. Taken together, these studies demonstrate that prejudice toward skilled immigrants from dissimilar cultures may contribute not only to employment discrimination, but also interfere with their attempts to seek justice when they proceed with a claim of discrimination.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".