Beyond Skills: An Integrative Approach to Doctoral Student Preparation for Diverse Careers
Bibliographic record
Abstract
An early consensus in the ongoing discourse about graduate student preparation for diverse careers was that graduates lacked competencies relevant to non-academic professional settings. Lists of missing “skills” were developed that universities and agencies sought to address, most commonly by the offering of generic (transferable) skills workshops or courses. In this paper, we critique this framing of the issue and discuss the limitations of the common approaches taken to address it. We propose a more integrated approach, where students’ thesis research itself is oriented to their possible futures (a practice already occurring in many areas), and where assessment of the competencies so developed is integral to the awarding of the degree. We illustrate the concepts through the stories of two students, and discuss policy ramifications and the substantial challenges to its realization presented by a highly competitive research environment and established ways of assessing success in faculty and students.
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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 | 0.038 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.028 | 0.051 |
| Scholarly communication | 0.028 | 0.017 |
| Open science | 0.004 | 0.038 |
| Research integrity | 0.006 | 0.013 |
| 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".