Bibliographic record
Abstract
Dans cet article, je compare la croissance économique des divers pays du G7 – le Canada, la France, l’Allemagne, l’Italie, le Japon, le Royaume-Uni et les États-Unis. Ces comparaisons s’articuleront autour des répercussions de l’investissement dans les technologies de l’information et les logiciels au cours de la période 1980-2001. En ayant recours aux prix internationaux harmonisés, j’ai analysé le rôle de l’investissement et de la productivité comme sources de la croissance dans les pays du G7 au cours de la période 1980-2001. J’ai subdivisé cette période de part et d’autre des années quatre-vingt-neuf et quatre-vingt-quinze, afin de pouvoir me concentrer davantage sur l’époque la plus récente. J’ai décomposé la croissance de la production de chaque pays en accroissement des intrants et en hausse de la productivité. Enfin, j’ai réparti l’augmentation des intrants entre les investissements dans les biens corporels, particulièrement dans le domaine des technologies de l’information et des logiciels, et dans le capital humain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".