Bibliographic record
Abstract
Purpose This paper aims to examine the “darker side” of what it means to engage in an international academic career. Extending beyond well‐documented themes relating to the difficulties of cross‐cultural adjustment and unfulfilled expectations/opportunities for promotion, this paper seeks to introduce “transience and risk” as two important dimensions of this very specific career choice. The paper draws especially on the contemporary “new” careers literature, including conceptions of career exploration as a framework to understand the research findings. Design/methodology/approach The paper employs a qualitative methodology, drawing on semi‐structured interviews conductedin situwith 30 expatriate academics in four different countries. Findings Transience and risk were identified as two important dimensions of the “darker side” of pursuing an international academic career. However, these two dimensions also had further positive aspects, thus signalling the complex and often contradictory nature of this specific career form. Research limitations/implications Introduces a more cautionary note to the contemporary literature on international careers and career exploration more generally. Practical implications Careers that evolve across international boundaries require support that extends beyond cross‐cultural training. Originality/value The paper contends that in as much as an international academic career offers a broad range of opportunities for fulfilment it also presents significant challenges that should be acknowledged.
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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".