The role of intention in post-PhD career decision-making
Bibliographic record
Abstract
Little is known about the journey from completion of the PhD to a range of post-PhD employment and careers. This paper addresses the role of personal intention in career decision-making and how intention interacts with personal values, goals and responsibilities as well as career opportunities. It addresses the question: What patterns, if any, were there in individuals’ career intentions and career decision-making over time? The research employs an identity development lens situated in a narrative approach to inquiry, thus focusing the research on the perspective of the individual. The analysis draws on an extensive database developed using a longitudinal research design that spanned 10 years and followed 48 individuals transitioning into a range of post-PhD positions and careers. On an annual basis, we collected biographical information, weekly activity logs, a pre-interview questionnaire, and an interview. This cycle was repeated from 4 to 7 times for each individual with a final follow-up one year after the main course of data collection was completed. Six patterns of post-PhD journeys were identified that have pedagogical implications with the potential to improve supervision, departmental and institutional career support for students.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".