User Guide for Statistics Canada's Annual Multifactor Productivity Program
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it