Postdoctoral scholars in a faculty of education: Navigating liminal spaces and marginal identities
Bibliographic record
Abstract
The last decade has seen a slow but steady increase in the number of postdoctoral scholars employed in faculties of education. In this article, seven postdoctoral scholars who worked in the same Canadian faculty of education explore their past positionings within the postdoctoral space. We share personal narratives related to issues of agency and identity in our relatively ill-defined positions. Similar to other early career academics, our reflections expose key concerns surrounding clarity of expectations, workload and work/life balance, and issues related to community and collegiality. In addition, we identify institutional or structural constraints that need to be reconciled in order to support postdoctoral scholars in their aspirations for success on personal and institutional levels. We provide recommendations and invite dialogue with regard to this emerging role in faculties of education.
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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 | 0.024 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.059 | 0.055 |
| Scholarly communication | 0.019 | 0.007 |
| Open science | 0.005 | 0.026 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".