Bibliographic record
Abstract
Purpose Although more than half of the PhD graduates do not take up traditional academic positions, the little we know of how they navigate into the non-academic workforce is somewhat conflicting. This study aims to contribute to our knowledge by examining over time the experiences of post-PhD social scientists who went into non-academic careers. It examines how post-PhD social scientists in non-academic careers characterize their experience of the PhD; how they imagine their post-PhD careers during the degree and how this influenced their doctoral activity; and to what extent their intentions changed over time and how agentive they were in managing challenges or disappointments. Design/methodology/approach The study uses a longitudinal qualitative narrative approach to examine the experience of eight post-PhD social scientists beginning during their degrees through their initial years after graduation outside academia. Findings The analysis highlights variation in clarity of career vision, strategic career thinking and action, knowledge of career opportunity structures and changes in career intentions over time. Still, for all individuals, the PhD was considered a powerful learning experience which continued to influence their lives. Practical implications Overall, the results make clear that post-PhD trajectories are best built from the beginning of the PhD, a conclusion that has curriculum implications. Originality/value This study incorporates the career question into the development of junior researchers highlighting the need to attend not only to objective measures of career success but also subjective intentions, investments, choices and assessments. Further, the constructs developed within an academic work context to understand career trajectories proved robust in analyzing non-academic work experience.
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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".