Les obstacles à l’innovation dans les industries de services au Canada
Bibliographic record
Abstract
De nombreux travaux se sont penchés sur les conditions qui favorisent les efforts en matière d’innovation technologique dans les entreprises canadiennes. L’objectif de cette étude est d’examiner l’innovation sous l’angle opposé, à savoir les obstacles perçus à l’innovation. Nous examinons les obstacles dans les industries des communications, de la finance et des services techniques. Les données proviennent de l’enquête innovation de 1996 menée par Statistique Canada. Premièrement, nous essayons de faire ressortir quelques facteurs expliquant la perception des obstacles à partir d’une analyse des données et d’un modèle économétrique. Deuxièmement, nous cherchons à déterminer dans quelle mesure certains obstacles sont complémentaires entre eux. Si complémentarité il y a, il faut adopter une approche systémique aux barrières à l’innovation pour y remédier efficacement.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".