Notes from the Trenches: Reflections from Recent PhD Graduates on Navigating the Academy
Bibliographic record
Abstract
PhD planning graduates face an increasingly competitive academic job market. In this commentary, seven recent graduates provide qualitative descriptions of the complicated and ever-changing expectations graduates face. We situate this within a larger reflection on the neoliberal academy that promotes a culture of competitiveness over care and production over purpose. We emphasize how this system is seemingly antithetical to the transformative planning work needed to address the most pressing planning issues of our time and provide suggestions for meeting shifting expectations, evolving training and support needs, and opportunities for a more compassionate tenure-track market. Our commentary has implications for doctoral pedagogy, the tenure-track market, and the academy.
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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 | 0.031 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.026 | 0.026 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.014 | 0.041 |
| 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".