Repatriate career exploration: a path to career growth and success
Bibliographic record
Abstract
Abstract Purpose – The purpose of this paper is to examine repatriate career exploration as a continuing growth‐oriented process and introduce repatriate hope as its crucial driver. Design/methodology/approach – Through a review of relevant literature, the framework of hope theory is introduced to argue for a more "agentic" view of the repatriate that can act as an independent career agent in the increasingly boundaryless career environment. Findings – The paper extends current knowledge of the repatriation process by describing ways in which repatriate hope drives career exploration toward valued outcomes of career growth and career success. It is also described how this repatriate career success will depend on the repatriate expectations and the social and organizational support received by the repatriate. Originality/value – The main contribution of this paper is a new view of the repatriation process through the lens of the hope theory that emphasizes positive psychological perspective indicating career growth/success as a valued outcome of repatriate career exploration process. Thus, repatriate is viewed as a proactive individual managing his or her career success and using exploration as a means of coping with and adjusting to a shifting set of challenges presented by the dramatic role change.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".