Phd careers beyond the traditional: integrating individual and structural factors for a richer account
Bibliographic record
Abstract
More than half of PhD graduates work outside academia. Yet we know little of the nature of their post-PhD careers and the conditions influencing them. Further, research to date tends to focus on either individual factors (e.g., graduate perceptions of PhD skills used) or structural factors (e.g., organizational interest in hiring PhDs). Few studies examine the intersection between individual and structural factors that actually influences career trajectories. Thus, this study was an exploratory examination of UK and Swiss non-traditional PhD careers in which we conceptually and empirically linked structural factors to individual experiences. The results provide a richer, more nuanced picture of PhD career trajectories, showing, for instance, how structural factors like distinct national economic climate and employment patterns intersected with individual factors like job-seeking strategies and job selection. The study’s originality lies in a narrative cross-case approach that merged empirical evidence from interviews with secondary data. We conclude by assessing the value of using such an integrative framework as well as suggesting areas for future research.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".