Reconfiguration des liens de collaboration entre acteurs industriels et universitaires de la recherche en intelligence artificielle à Montréal et à Toronto.
Bibliographic record
Abstract
<p>Le champ de l’intelligence artificielle (IA) a connu au courant de la dernière décennie une accélération marquée de son développement qui s’est notamment traduite dans une réarticulation des liens de collaboration entre les acteurs industriels et universitaires des écosystèmes d’IA de Montréal et de Toronto. S’inscrivant dans le champ des études des sciences et des technologies, ce mémoire mobilise les développements de la sociologie de la traduction et des études de la gouvernementalité en vue d’établir comment le positionnement institutionnel d’un espace de production de la recherche au sein de son écosystème de référence détermine la configuration interne de cette unité et, en dernière instance, la structure d’incitatifs avec laquelle doivent composer les chercheurs y opérant. Additionnellement, on porte attention aux coalitions d’acteurs et répertoires de tactiques mobilisés dans le contexte des campagnes de consultation publique entourant la Déclaration de Montréal pour un développement responsable de l'IA et le projet Quayside Toronto. À cette fin, douze entretiens ont été menés avec des représentants des deux écosystèmes, en plus d’une revue systématique des quotidiens québécois et ontariens et une veille médiatique portant sur différentes publications spécialisées. En définitive, on parvient à articuler les conditions structurelles responsables de l’identification plus forte des représentants de l’écosystème montréalais vis-à-vis d’une programmatique normative engagée envers un développement « éthique et responsable » de ce champ technologique. <br /><br /> Over the last decade, the field of artificial intelligence (AI) has experienced a pronounced acceleration in its development, which in turn has resulted in the re-articulation of the collaborative links between industrial and academic actors in the AI ecosystems of Montreal and Toronto. This thesis, which falls within the field of science and technology studies, mobilizes developments in the sociology of translation and governmentality studies to establish how the institutional positioning of a research production space within its reference ecosystem determines the internal configuration of this unit and, ultimately, the incentive structure with which its researchers must operate under. In addition, attention is paid to the coalitions of actors and repertoires of tactics mobilized in the context of the public consultation campaigns surrounding the Montreal Declaration for a Responsible Development of AI and the Quayside Toronto project. To this end, twelve interviews were conducted with representatives of the two ecosystems, in addition to a systematic review of Quebec and Ontario daily newspapers and a media monitoring of various specialized publications. In the end, we articulate the structural conditions responsible for the stronger identification of the representatives of the Montreal ecosystem with a normative program committed to an "ethical and responsible" development of this technological field.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".