Reconnaître le travail des artistes à l’emploi à l’université: Le financement de la recherche et de la création au Québec et au Canada
Bibliographic record
Abstract
This contribution aims to identify and better understand the consequences of what we will call acts of persuasion within the university ecosystem in Canada. Taking into account sociological, political, and ethical factors, as well as the facts revealed by this scientific work, this article identifies four key areas of study: 1) an ambiguity in the composition of grant evaluation panels within research funding bodies, leading to 2) confusion in the use of research funds allocated to creative projects and, in turn, generating 3) a troubling confusion (this time of a semantic nature) in the definitions of research and creative projects (and consequently of "research-creation"), all of which is further fueled by 4) administrative problems and possible collusion that persist within this ecosystem between artists and decision-making administrators. Based on these markers, which point to a complex and thorny situation, the article outlines possible solutions to better understand the place and role of artistic culture at the university, and to recognize the contribution of artists at its true value, in particular by proposing a more appropriate way of managing research and creation funds, especially for students.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.010 |
| Scholarly communication | 0.015 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".