Comment on ‘Estimating Capital Input for Measuring Business Sector Multifactor
Bibliographic record
Abstract
This article shows that the large difference of 0.8 per cent percentage points per year between top-down estimates of capital input growth obtained by Diewert and Yu and the bottom-up estimates produced by Statistics Canada for the Canadian business sector over the 1961-2011 period can be interpreted as an reallocation effect due either to inefficiencies in production and or a measurement issue. It is also noted that although user costs of capital are officially recognized in the System of National Accounts, there is no single recommendation on the details of implementation and that moving towards such a recommendation is an objective worth pursuing. RÉSUMÉ Cet article montre que la grosse différence de 0,8 points de pourcentage par année entre les estimations descendantes de la croissance des intrants de capital obtenues par Diewert et Yu et les estimations ascendantes produites par Statistique Canada pour le secteur des entreprises canadiennes pendant la période 1961 à 2011 peut être interprétée comme un effet de redistribution dû à des pratiques de production inefficaces. On fait remarquer aussi que, bien que les coûts de l'utilisation du capital soient officiellement reconnus dans le Système de comptabilité nationale, il n'y a pas une seule recommandation sur les détails de la mise en œuvre, et qu'en arriver à formuler ce genre de recommandation est un objectif qu'il vaut la peine d'essayer d'atteindre. GU (2012) IN THIS SYMPOSIUM examines the dif-ferences in capital input estimates between the
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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 | 0.018 | 0.146 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".