Postdoctoral positions as preparation for desired careers: a narrative approach to understanding postdoctoral experience
Bibliographic record
Abstract
Doing a ‘postdoc’ following a doctorate is becoming more and more common worldwide as the pre-tenure job market continues shrinking in relation to the number of PhD graduates. Yet, behind statistics and descriptions of collective experience, how individuals experience the postdoctoral period is largely unknown, especially how they use this phase as preparation for future employment. Drawing on longitudinal data, this paper provides a close look at how seven postdoctoral scholars in life sciences from two Canadian universities intentionally prepared for their desired careers through day-to-day activities. The participants’ daily activities were situated in three ways: intellectual, networking and institutional. It was found that they were all agentive in preparing for the future; yet, agency was exercised differently due to different institutional and personal contexts. The personal was found to be a significant factor that influenced their career preparations and decisions. This study addresses the gap in the literature regarding postdoctoral experiences and enriches our understanding about postdoctoral experience and training.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.018 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".