Love <i>acts</i> and revolutionary praxis: challenging the neoliberal university through a teaching scholars development program
Bibliographic record
Abstract
There has been significant interest in developing academics through Teaching Scholar Development Programs across the USA, Canada, the UK, and more recently in Australia. At their core, such programs develop academics across teaching scholarship, leadership, promotion, and award opportunities, where universities reap the benefits of developing such a cadre of leaders. This paper pays witness to one such a program in an Australian university to highlight enactments of caring passionately. We use qualitative survey evaluation data, metaphor analysis and reflective practice to nuance the pleasures, passions and challenges of the lived experiences using phenomenological and metaphor lenses to describe our experiences. Metaphors provide powerful insights into the dimensions of experience as they open up how programs are perceived and experienced. Our paper disrupts traditional linear writing through rhizomatic, multivocal and multitextual encounters to challenge dominant authorial voicing. The academic identity work and emotional work required in the program is unfolded through evolving, experiencing and reflecting on the program to inform design and highlight what we have come to (re)value in our academic work when we come together to learn, share, and lead. We forge ways to be and become with and against neoliberal agendas that have choked the soul of ‘the university’ to evolve rich spaces and practices of/for reciprocity and kindness where not only learning can thrive, but where love acts – a much needed revolutionary praxis for our time.
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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 | 0.018 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.045 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.002 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".