The protest intentions of skilled immigrants with credentialing problems: A test of a model integrating relative deprivation theory with social identity theory
Bibliographic record
Abstract
Currently in Canada, there is a widespread underemployment among foreign-trained immigrants. This is because potential employers often have to evaluate unfamiliar foreign qualifications that, regrettably, are often misunderstood and undervalued. This questionnaire study tested a model that integrates relative deprivation theory with social identity theory to predict the degree to which skilled migrants from Asia and Africa with credentialing problems protest this systemic discriminatory barrier (N=180). In the model, the strength of cultural and national identifications are conceptualized as opposing motivational forces that, along with collective relative deprivation (CRD), directly impact protest intentions. As well, the model specifies that the so-called 'affective' component of CRD consists of an attribution of blame (perceived discrimination) and associated emotions. Structural equation modelling shows that the model is a good fit to the data. As hypothesized, significant path coefficients show that the strength of cultural identity increases and the strength of Canadian identity decreases the degree to which the respondents feel that immigrants suffer discrimination which, in-turn, influenced their intentions to take protest actions. The implications of these findings for the integration of relative deprivation and social identity theories are discussed.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".