Bibliographic record
Abstract
Purpose The international mentoring literature predominantly features traditional company-assigned expatriates as protégés overlooking other types of global talent, such as immigrants, refugees, and international graduates, who may help organizations gain long-term IHRM competitive advantages. We integrate multidisciplinary research to better understand the role of mentoring as a global talent management tool, identify research gaps, and propose future research directions. Design/methodology/approach We draw on an integrative review of 71 academic journal articles published between 1999 and 2024 to explore the role of mentoring in managing global talent (i.e. expatriates, immigrants, refugees, and international students and graduates). Findings We found that research has identified and examined relationships between various antecedents and outcomes of mentoring but mainly treating mentoring as a talent development tool. Less is known about the role of mentoring as a recruitment and selection tool in the pre-employment context. Mentoring is an important HRM tool that contributes to managing a global talent pool and developing existing employees. Originality/value The review contributes to a better understanding of the characteristics and processes involved in mentoring in a global context by proposing a framework that incorporates antecedents of mentoring, characteristics of the mentoring process, and mentoring outcomes. It highlights the value of mentoring as a recruitment and selection tool supporting global talent management and identifies avenues for future research.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".