Challenging the taken-for-granted: how research analysis might inform pedagogical practices and institutional policies related to doctoral education
Bibliographic record
Abstract
Taken-for-granted pedagogical practices and institutional policies are often built without evidence of effectiveness, or can result from external calls for accountability that are often accepted given the lack of evidence to challenge them. We argue the need for evidence-based perspectives to support the rethinking of such practices and policies related to doctoral education and potentially to challenge external drivers that are placing increasing demands on academics. We have been particularly attentive to using our research findings for this purpose, and in this article we describe two examples of how we have drawn evidence from our research that challenges the taken-for-granted. We hope that describing our approach may stimulate other researchers to emphasize the specific implications of their research findings as regards influencing change towards more research-informed institutional practices and policies.
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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 | 0.658 | 0.604 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.024 | 0.178 |
| Scholarly communication | 0.070 | 0.082 |
| Open science | 0.009 | 0.035 |
| Research integrity | 0.022 | 0.022 |
| Insufficient payload (model declined to judge) | 0.004 | 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".