Le ralentissement du chercheur.e : comment résister à la corporatization de l’université - Slowing down as an academic: How to resist the corporatization of the university
Bibliographic record
Abstract
Cet atelier, s’adresse autant aux etudiant.es qu’aux chercheur.es experimente.es. Il propose de reflechir de maniere critique et creative aux questionnements qui entourent un certain « emballement » de la recherche. Le mouvement de la slow research (ralentissement de la recherche) et d’autres publications relatives au « slow professor » ( professeur en ralentissement ) temoignent de l’actualite de tels questionnements (Berg et Seeber, 2016; Horgan, 2011). Il s’agira donc d’analyser de facon critique les structures d’organisation et de production qui encadrent le travail de chercheur.e, qu’il soit professeur.e ou etudiant.e-chercheur.e. Des pistes de solutions seront abordees et discutees en mobilisant des cadres alternatifs, par exemple celui de la decroissance (Bayon, Flipo et Schneider, 2010), et la creation des reseaux de soutien et de confiance (Berg et Seeber, 2016). Notre objectif est de partir plus conscient.es des enjeux et plus outille.es pour resister et pour mettre en pratique une vie academique equilibree et saine. This workshop, targetting both grad students and experienced academics, is designed to stimulate critical and creative reflection about slowing down research. The slow science movement, and similar publications attest to the timeliness of this discussion (Berg & Seeber, 2016; Horgan, 2011). We will cricially analyze the organisation and corporization of the university and the conditions which lead researchers, both professors and students, to individualize increasing demands on their time. Proposed solutions will be examined, mobilizing alternative frameworks, such as decreasing productivity (Bayon, Flipo & Schneider, 2010), and the creation of “holding environments” based on trust and support (Berg & Seeber, 2016). Our objective is to end the workshop more conscious of the challenges to our academic integrity imposed by the current system, and better equipped to enagage in a balanced and healthy academic life.
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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 | 0.037 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.051 |
| Scholarly communication | 0.024 | 0.031 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.014 | 0.016 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".