Introduction to the special issue on employment discrimination against immigrants
Bibliographic record
Abstract
Purpose This editorial aims to introduce the special issue on employment discrimination against immigrants. Design/methodology/approach The first part is a commentary on key issues in the study of employment discrimination against immigrants. The second part presents the five articles in the special issue. Findings The papers in this special issue focus on a variety of issues associated with employment discrimination against immigrants. For example, they consider: discrimination based on accents; differences among justice perceptions among immigrants and non‐immigrants; the effects of negative stereotypes on workplace outcomes; the treatment of Hispanic immigrants; and the reasons for the lack of research on Hispanic immigrants. Research limitations/implications The author comments on key issues that researchers of employment discrimination against immigrants have to take into account. These issues include: the appreciation of the diversity among immigrants; an understanding of the complexity of employment discrimination research; openness to cross‐disciplinary approaches; and the consideration of employment discrimination within the context of the immigrant experience. The five articles that make up the special issues vary in their nature (empirical, critical), methodologies (quantitative, qualitative), locations (United States, Germany, and Canada), and implications. Practical implications The issues discussed in the papers have important implications for understanding and overcoming employment discrimination against immigrants. Originality/value The Journal of Managerial Psychology invited this special issue to initiate psychological research on employment discrimination against immigrants. The intent is to draw the attention of organizational scholars to the large, yet under‐studied immigrant segment of the workforce.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.038 | 0.010 |
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".