Firm and Individual Level Processes Shaping Migrant and Refugee Integration
Bibliographic record
Abstract
This symposium explores the multifaceted experiences of migrants and refugees as they navigate the challenges and opportunities of employment, as well as the critical role organizations play in shaping employment and well-being outcomes. We offer a curated selection of papers, each exploring an important process, either at the organizational or the individual level, and collectively enhancing our understanding of the pathways to successful integration and to fostering more inclusive societies. A collaboration of scholars from universities in Australia, Austria, Canada, and the USA, it includes both theoretical and (quantitative and qualitative) empirical papers, each contributing to the conversation about the integration process of migrants and refugees. Why do some firms become more diverse, while others linger Author: Betina Agata Szkudlarek; The University of Sydney Author: Christine Han; Skilled migrants’ symbolic resources and diversity work in small and medium enterprises Author: Viktoriya Voloshyna; Thompson Rivers University Author: Jelena Zikic; York University From resources to resourcing: Qualified immigrants’ career transitions post-migration Author: Dunja Palic; Carleton University Author: Luciara Nardon; Carleton University Author: Amrita Hari; Carleton University When being proactive can cost you Author: Viktoriya Zipper-Weber; Danube University Krems Author: Mihaela Dimitrova; WU Vienna University of Economics and Business Author: Mila Borislavova Lazarova; Simon Fraser University Disentangling immigrant employees’ acculturation strategies, authenticity, and work-family interface Author: Maggie Wan; Texas State University Author: Margaret A. Shaffer; University of Oklahoma
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 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".