In Search of a Community: Navigating the Academic Spaces of Belonging as a Postdoctoral Fellow
Bibliographic record
Abstract
Abstract Postdoctoral fellowships are an important career phase for early career researchers. This part of one's career is often characterised by stress, loneliness and anxiety about the future. Moreover, postdoctoral fellowships are, by definition, individualistic and career oriented. We ask: how do postdoctoral fellowships provide the means for an academic sense of belonging – if they do? In this chapter, we explore this complex question by introducing two personal narratives of navigating the spaces of belonging (and not belonging) during postdoctoral fellowships. First, the first author (Juuso) explores his experiences as a fellow in two postdoctoral programmes. Next, the second author (Robyn) provides a supervisor's reflection. We analyse these narratives with the theoretical lens of a sense of belonging, understood as an affective, physical, social and political phenomenon. Our narratives shed light on how belonging is built within postdoctoral fellowships' often cold and lonely structures. We particularly discuss the spaces of non-belonging that might simultaneously empower and disempower postdoctoral fellows (as well as their supervisors).
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".