Bibliographic record
Abstract
Having the flexibility to pursue an international career is increasingly common in many professions. Based on two qualitative studies of international academics, this paper focuses on academia. Commencing with a discussion about the different dimensions of flexibility in academia it focuses specifically on geographic flexibility, understood as the ability to pursue a career across international boundaries. Drawing on conceptions of an international community of scholars operating in a science context and specific national and institutional contexts the paper explores the experiences of international academics. It also considers the ‘modes of engagement’ they use to navigate the demands of those contexts. The findings suggest that while academia as a profession may be characterized by geographic flexibility a certain tension exists between academia and the national and institutional contexts within which academics must operate. It is noted that it is internationally mobile academics who are currently paying the price of those tensions and offers a cautionary note to those who are contemplating such a career move. It also suggests that academics can adopt certain ‘modes of engagement’ in order to maintain, transform or subvert those institutional challenges.
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 | 0.048 | 0.116 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.043 |
| Scholarly communication | 0.014 | 0.032 |
| Open science | 0.013 | 0.011 |
| Research integrity | 0.021 | 0.054 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".