Challenging Performative Fabrication: Seeking authenticity in academic development practice
Bibliographic record
Abstract
This paper explores tensions between individual desires to enact the work of academic development practice in ways that foster authenticity, and the pressure to fabricate proper identities in the service of the performative university. Through auto‐ethnographic inquiry, three academic developers together ask, “How are we and our practices true to who we are, to our colleagues and university, and to the purposes of higher education?” Our writing aims to encourage reflection on the moral and ethical dimensions of the work of academic development. Our discussion identifies particular individual and collective strategies of healing and transformation that will lead academics, and academic developers, toward a more authentic practice. Cet article explore les tensions existant entre les désirs individuels de « pratiquer » le développement pédagogique de façon à susciter l’authenticité, et la pression visant à forger des identités propres au service de l’université performative. Par l’entremise d’une étude auto‐ethnographique, trois conseillers pédagogiques se sont posé la question : « Comment, nous et nos pratiques, sommes‐nous fidèles à nous‐mêmes, à nos collègues et à notre université, ou aux buts de l’enseignement supérieur ? ». Notre article vise à encourager la réflexion au sujet des dimensions morales et éthiques du travail de développement pédagogique. Notre discussion identifie des stratégies individuelles et collectives de réparation et de transformation qui mèneront les universitaires et les conseillers pédagogiques vers une pratique plus authentique.
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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 | 0.041 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.022 | 0.092 |
| Scholarly communication | 0.020 | 0.016 |
| Open science | 0.003 | 0.029 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".