Universities as Spaces of Possibility: Towards More Creative, Caring Academic Labour
Bibliographic record
Abstract
ABSTRACT In Spring 2025, we had the opportunity to read, write discussion questions and organise a conference panel for the book Higher Expectations: How to Survive Academia, Make it Better for Others, and Transform the University by Roberta Hawkins and Leslie Kern, published in 2024 by Between the Lines Press, Toronto. We write this commentary to compel geographers to read this book and to share an example of how reading this book encouraged us to construct our academic conference labour differently, offering our discussion questions to facilitate discussion groups for other readers. We see the book as a guide for the current moment in higher education, including the ongoing neoliberalisation of the academy and limits on academic freedom, processes that structure our everyday university labour. In this commentary, we draw on our own experiences of burn out, combined with the book's call for more caring academic labour practices, to invite readers to rethink, reframe, to do and be otherwise, in their academic journeys. In highlighting all the ways, we can change our academic labour, the book recalls an energising space of possibility, a space from which we might reimagine the university.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.028 | 0.094 |
| Scholarly communication | 0.035 | 0.026 |
| Open science | 0.002 | 0.032 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".