Entre économie politique des controverses et visibilités controversées, la construction médiatique de l’intelligence artificielle au Canada et au Québec
Bibliographic record
Abstract
Ce mémoire présente une trajectoire du développement de l’intelligence artificielle (IA) au Canada et au Québec. En prenant pour objet d’étude la construction médiatique de l’IA, la recherche s’appuie sur 14 entrevues réalisées à l’été 2021 dans le cadre du projet de recherche Shaping AI. L’objectif de cette recherche est de comprendre l’interrelation entre travail journalistique et organisation du milieu de l’IA. Pour ce faire, ce mémoire présente une méthodologie mêlant une approche multidisciplinaire qui met l’accent sur l’aspect situé de la co-construction du social et de la technologie à travers sa médiatisation dans la période 2012-2021. Dans les premiers temps de la couverture, si l’IA était principalement utilisée comme mot clé pour présenter une révolution à venir, l’absence de résultats concrets a vite contribué à renverser la balance et mettre en avant les échecs remettant en question le développement de l’IA. Ces épisodes de controverses ont participé à un réajustement des acteurs participants à ce développement. Dans le contexte canadien et québécois, les investissements massifs dans le développement des nouvelles technologies d’IA principalement au Québec ont contribué à en faire une place privilégiée pour observer ses dynamiques. Montréal se présente ainsi comme un écosystème central de l’IA à l’échelle nationale et internationale faisant de cette ville un endroit propice à l’étude des dynamiques du développement de l’IA. \n \nThis paper presents a trajectory of AI development in Canada and Quebec. Taking as its object of study the media construction of Artificial Intelligence (AI), the research is based on 14 interviews conducted in the summer of 2021 as part of the Shaping AI research project. The dual challenge of this research is to understand the interrelationship between journalistic work and the organization of the AI community. To do so, this paper presents a methodology blending a multidisciplinary approach that emphasizes the situated aspect of the co-construction of social and technology through its mediatization in the period 2012–2021. In the early days of the coverage, while AI was mainly used as a catchword to present a coming revolution, the lack of concrete results soon helped to tip the balance and highlight failures questioning the development of AI. These episodes of controversy have contributed to a readjustment of the actors involved in this development. In the Canadian and Quebec context, the massive investments in the development of new AI technologies, mainly in Quebec, have contributed to make it a privileged place to observe its dynamics. Montreal is thus a central ecosystem for AI on a national and international scale, making it an ideal place to study the dynamics of AI development.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.012 | 0.021 |
| Scholarly communication | 0.023 | 0.011 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.024 | 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".