Généralisation de l'usage du Big Data en finance de marché, entre mythes et réalités: Une approche par le travail institutionnel
Bibliographic record
Abstract
Résumé Cette recherche examine l'utilisation du Big Data, en particulier des algorithmes de trading basés sur l'intelligence artificielle, dans le domaine de la finance de marché. À travers une analyse lexicométrique d'un corpus de données provenant de la presse financière sur une période de 10 ans, nous avons identifié quatre acteurs qui contribuent à l'institutionnalisation de l'utilisation du Big Data dans la finance de marché: les experts issus du secteur bancaire et financier, les intellectuels (académiques, journalistes, écrivains), les gestionnaires à la recherche de ressources et les institutions publiques. Alors que les dilemmes éthiques et les problèmes de qualification sont une réalité, seules les institutions publiques les mettent en avant dans leur discours, contribuant ainsi au processus d'institutionnalisation.
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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 | 0.013 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".