Constructing post‐PhD careers: negotiating opportunities and personal goals
Bibliographic record
Abstract
Purpose Until relatively recently, the doctorate was generally perceived as preparation for a full‐time permanent academic position. However, this is no longer the case, with many PhD graduates working outside academia or in temporary full‐ and part‐time positions in higher education institutions. Yet, we know little of the ways in which they perceive and then navigate the transition from PhD to initial careers. Thus authors undertook an analysis of longitudinal data from six social sciences PhDs (part of a larger dataset) to document how they transitioned from the PhD and navigated a future. Design/methodology/approach Different forms of data, collected multiple times over two years, were analysed using emergent coding to capture the experiences of navigating a future. Findings The results enrich present understanding of this end‐of‐PhD period, in particular, highlighting individuals' growing understanding of academic, hybrid and non‐academic career opportunity structures, and the importance of personal intentions and relationships in defining possible horizons for action. Originality/value The conceptual and pedagogical contributions of this study to understanding doctoral and post‐doctoral career decision‐making are described.
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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 | 0.026 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| 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".