Les gains de productivité au moyen de l’usage des technologies de l’information : l’expérience australienne
Bibliographic record
Abstract
Cette étude fait appel à un cadre de la comptabilité de la croissance pour comparer la contribution des technologies de l’information à l’accélération de la productivité du travail en Australie et aux États-Unis. En utilisant les États-Unis comme repère, la présente étude attribue jusqu’à 0,3 point de pourcentage de cette accélération de 1 point de pourcentage aux technologies de l’information. Les technologies de l’information n’ont pas eu d’effet net sur l’intensité du capital puisque leur hausse a remplacé les autres formes de capital. La contribution des technologies de l’information est attribuable à la restructuration des entreprises et à l’innovation de produits et procédés qu’elle a rendue possible. Jusqu’ici les gains ont été concentrés dans les services de la distribution (particulièrement le commerce de gros) et des services financiers.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".