Bibliographic record
Abstract
In order to measure industry total factor productivity accurately, we require reliable information not only on the outputs produced and the labour input utilized by the industry but we also require accurate information on eight additional classes of input used by the industry. One of these additional classes of input is intermediate input; i.e., inputs that are utilized by the industry but which are produced by other industries. Information on the real and nominal purchases of intermediate inputs by industry comes from the system of input-output tables published by Statistics Canada. In section 4, we explain why the estimates of real intermediate input utilization by industry that one can obtain from the real input-output tables of any country are likely to be inaccurate. In section 5, we go on to make the case that national productivity estimates are likely to be more accurate than subnational industry estimates. Section 6 concludes on an optimistic note. The total factor productivity of a firm, industry or group of industries is defined as the real output produced by the firm or industry over a period of time divided by the real input used by the same set of production units over the same time period. However, it turns out to be difficult to provide a meaningful definition of real output or real input due
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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 | 0.036 | 0.161 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".