The Professional Credentials of Immigrants: A Status-and-Expectations Approach
Bibliographic record
Abstract
Abstract Purpose (a) To examine “native-born/immigrant” (nativity) and “national/foreign professional credentials” (country of credentials) as status factors in terms of expectation states theory, and (b) to lay out a blueprint for a theory-based, experimental research agenda in this area. Design/methodology/approach (for (b) above). I propose a research program based on three types of expectation states experimental designs: the open group-discussion, the rejection-of-influence standardized setting, and the application-files format. All three incorporate measures of either biased evaluations or double standards for competence, or of both. I illustrate how these designs can be adapted to assess, through the presence/absence of one or the other of those practices, the separate impacts of nativity, country of professional credentials and selected additional factors on the inference of task competence. The need for and the advantages of systematic, experimental work on this topic are highlighted. Findings (from (a) above). I review evidence of the status value of nativity and country of credentials through data on evaluations, employment, and earnings. My evidence originates in contemporary Canadian studies that present results from surveys, interviews, census records, and – to a lesser extent – experiments, and these findings support my claim. Practical/social implications The proposed research will facilitate the development of interventions toward the standardized and unbiased assessment of immigrants’ foreign credentials. Originality/value The agenda I put forth constitutes a novel approach to the study of nativity and country of credentials. The work will extend the expectation states program, and enhance immigration research both theoretically and methodologically.
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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 | 0.019 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".