Critical Reflections on Mental and Emotional Distress in the Academy
Bibliographic record
Abstract
A rising number of students seeking mental health services across university campuses in Europe and North America has prompted faculty, administrators and student service providers to call attention to what some describe as a crisis. Academic geographers, however, have not yet begun to explore a collective and professional response to this crisis. In this article, we seek to examine what a critical commitment to addressing emotional and mental distress in the academy might look like, discussing the different understandings of what is meant by mental health and its manifestations in the academy as the ‘new normal’. We seek to understand the crisis in mental and emotional distress through a portrayal of the neoliberalization of the academy and conclude by imagining a different kind of academy, exploring how the spatialized practices that produce it can be differently enacted.
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.039 | 0.107 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.009 | 0.029 |
| 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".