Professional learning and development framework for postdoctoral scholars
Bibliographic record
Abstract
Purpose Many postdoctoral scholars are seeking professional learning and development (PLD) opportunities to prepare for diverse careers, roles and responsibilities. This paper aims to develop an evidence-informed framework for PLD of postdoctoral scholars that speaks to these changing career paths. Design/methodology/approach This paper used an integrated knowledge translation approach to synthesize and extend previous work on postdoctoral scholars’ PLD. The authors engaged in consultations with key stakeholders and synthesized findings from literature reviews, surveys and semi-structured interviews to create a framework for PLD. Findings The PLD framework consists of four major domains, namely, professional socialization; professional skills; academic development; and personal effectiveness. The 4 major domains are subdivided into 16 subdomains that represent the various skills and competencies that postdoctoral scholars can build throughout their postdoctoral fellowships. Originality/value The framework can be used to support postdoctoral scholars, postdoctoral supervisors and higher education institutions in developing high quality, evidence-informed PLD plans to meet the diverse career needs of postdoctoral scholars.
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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 | 0.056 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.011 | 0.022 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".