Can you tell me more about your postdoctoral experience? A personal narrative review of the postdoctoral position in the social sciences
Bibliographic record
Abstract
Purpose This paper is the result of a collaboration and sharing of experiences of two postdoctoral researchers. The purpose of this paper is to put these experiences into perspective by cross-referencing our respective personal narratives with an analysis of the existing literature on the postdoctoral experience in the social sciences. Design/methodology/approach The authors conducted a non-exhaustive systematic literature review using the database PsycInfo and the multidisciplinary Web of Science Catalogue database to find relevant articles published from 2000 to today. Of the 946 articles identified from the database, only 12 were included in the literature review. The authors also included four articles identified from other sources, such as Google Scholar. Secondly, the authors used a method inspired by reflexive personal narrative writing, which allowed us to share our postdoctoral experience and examine how it compares or complements the existing literature on postdoctoral experience in the social sciences. Findings The literature highlights three significant criteria that play a major role in the postdoctoral experience across disciplines: professional identity, work–life balance and relationship with supervisor. While the majority of the current literature seems to highlight the importance of career prospects in the daily lives of postdoctoral researchers, the other two aspects seem to be somewhat less explored. However, personal factors as well as the relationship with the supervisor appear to be of major importance in the search for work–life balance, feelings of competency and overall satisfaction among postdoctoral researchers. Research limitations/implications At the theoretical level, this paper allows a better understanding of the experiences of postdoctoral students in the social sciences, which seem to be less documented than those in scientific fields (e.g., Science, technology, engineering and mathematics postdoctoral fellow). Practical implications On a practical level, it constitutes a tool for reflection for postdoctoral researchers in the social sciences as well as for academic actors working to support and develop the well-being of these researchers (e.g. teachers, supervisors, administrators), all with the aim of optimising academic practices. Originality/value These results are discussed with respect to the specificity that our subjective personal narratives can offer to understand postdoctoral experiences, particularly in the social sciences, and thus offer reflections on ways to attend to individual psychosocial and relational needs that can foster an improved personal and professional 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 | 0.040 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".