International human resource management in an era of political nationalism
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it