Listen Up! Be Responsible! What Graduate Students Hear About University Teaching, Graduate Education and Employment
Bibliographic record
Abstract
What we hear at universities and in public conversations is that there is a crisis in graduate student education and employment. We are interested here in the (re)circulation of the discourses of crisis and responsibility. What do graduate students hear about their education, their career prospects, and their responsibilities? How does work in educational development contribute to these conversations? We explore these questions through an analysis of two data sets: the course outlines for multidiscipline graduate courses on university teaching, and popular and academic press articles on graduate education and employment. Through this discursive analysis, we first examine what graduate students hear through these two archives of writing. We then unpack two key discourses that emerge across the archives: the privileging of practice over theory, and the desire to assign responsibility for how the crisis of graduate education and employment should be resolved and by whom.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.030 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.010 |
| 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; 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".