Professional learning and development initiatives for postdoctoral scholars
Bibliographic record
Abstract
Purpose Postdoctoral scholars are increasingly pursuing diverse career paths requiring broad skill sets. This study aims to create a more comprehensive understanding of current approaches and strategies for postdoctoral scholars professional learning and development. Design/methodology/approach This literature review is a systematic examination and synthesis of the current literature describing professional learning and development pertaining to postdoctoral scholars. The objectives and components of initiatives were extracted and narratively synthesized to identity important patterns and themes across the literature. Findings Commonalities amongst professional learning and development initiatives for postdoctoral scholars included skills development in the following areas: teaching and learning, mentorship, academic careers, academic writing, industry careers, networking, career planning, project management, time management, communication, leadership and balancing work-life demands. Originality/value In synthesizing the literature that describes professional learning and development opportunities for postdoctoral scholars, it is apparent that opportunities look different in every setting with no empirical evidence that one strategy is more effective than another. Given the significant resources often required to support professional learning and development initiatives, a deeper understanding of the benefits and deficiencies of various components is needed to ensure scarce resources are invested in the most effective strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.045 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".