Reframing our approach to doctoral programs: an integrative framework for action and research
Bibliographic record
Abstract
A serious problem exists in the academic world, namely doctoral education attrition rates that approach 50% in some disciplines. Yet, calls for action have generally been ad hoc rather than theory driven. Further, research has not been conceived and implemented with sufficient breadth to integrate factors influencing the outcomes across the societal/supra‐societal, institutional and departmental/disciplinary contexts. Concurrently, epistemological questions are being directed at the appropriateness of both the content and the process of doctoral programs. In this paper, we propose as a heuristic, an integrative framework of nested contexts to guide both research and action. The framework integrates the range of factors influencing doctoral student experience, so that we can envision responding to this issue in a coherent and effective fashion.
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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 | 0.233 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.028 | 0.154 |
| Scholarly communication | 0.042 | 0.040 |
| Open science | 0.010 | 0.035 |
| Research integrity | 0.021 | 0.026 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".