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
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
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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