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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".