Bibliographic record
Abstract
This paper shares findings from a qualitative study on university student mental health and illness that included digitally recorded interviews with university student services and programs professionals and staff at a Canadian university. Transcripts were thematically coded and analyzed using a disability studies informed interpretive sociological approach. Four key themes emerged: dwelling with disclosure, being open to the ‘nothing but’, understanding oneself as ‘not a counselor’, and coming to terms with the reality that under neoliberalism ‘we all fall’ Two key insights also emerged from the analysis: 1) Access to university-based programs and services is shaped by assumptions about productivity and reputation; 2) Psychiatric knowledge and expertise influences and informs how university student services staff understand and enact their roles within the university system. This paper considers how university-wide productivity-oriented psy-knowledge and practices organize and authorize what one participant described as a ‘hidden curriculum’ of academic success. This hidden curriculum manifests in the form of a referral-based resiliency (govern)mentality in university student service provision. It closes with a reflection on the transformative potential of adopting a “critically maladaptive” (McLaren, 2010, p. 504) approach that is attentive to alterity in university-based student services professional perspectives which appears in the form of a thoughtful “but…”.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.040 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".