International human resource management in an era of political nationalism
Bibliographic record
Abstract
In times of the “Brexit” and “America First” policies, several industrialized countries' governments are turning toward more national‐oriented migration policies. Simultaneously, societal aversion to immigration is growing. Both trends are sending negative signals to highly skilled employees and making immigrants feel that they are no longer welcome. Consequently, international careers are becoming uncertain, risky, and unpredictable. This new reality in industrialized knowledge‐based economies may affect firms' talent pool and the skill set available to a country. To shed light on the new environment of international human resource management, we interviewed Mary Yoko Brannen and David Collings, leading experts in the field, to explore their perspective on how the field is changing. The interviews reported here uncover fascinating insights, including the need to counteract the globalization fears in the West of the predominantly White working and lower‐middle class through education. Companies may also rethink their organizational boundaries and the notion of traditional employees by using their agility to counteract the political forces harming their talent pool strategy.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".