User Guide for Statistics Canada's Annual Multifactor Productivity Program
Bibliographic record
Abstract
The Canadian Productivity Accounts (CPA) of Statistics Canada maintain two multifactor productivity (MFP) programs. The Major Sector Multifactor Productivity Program develops the indexes of MFP for the total business sector and major industry groups in the business sector. The Industry Multifactor Productivity Program or the Industry KLEMS Productivity Program develops the industry productivity database that includes MFP indexes, output, capital (K), labour (L), energy (E), materials (M) and services (S) inputs for the individual industries of the business sector at various levels of industry aggregation. This paper describes the methodologies and data sources that are used to construct the major sector MFP indexes and the industry productivity database (or the KLEMS database). More specifically, this paper is meant to: provide a background of the major sector MFP program and the industry KLEMS productivity program; present the methodology for measuring MFP; describe the data sources and data available from the MFP programs; present a quality rating of the industry KLEMS productivity data; and describe the research agenda related to the MFP program.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.031 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.204 | 0.122 |
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".