Very preliminary. Do not quote Integrating Natural Capital in the Canadian Productivity Accounts* By
Bibliographic record
Abstract
Abstract: The productivity series for the mining sector reported by statistical agencies usually indicate either a negative or, at best, a lacklustre multifactor productivity growth. This performance, at odd with the anecdotal evidence on the dynamic nature of this sector, is the result of an inadequate production framework employed by the statistical system. This paper proposes an integration between the productivity accounts and the environment satellite accounts which allows for a production framework with the following desirable features: a) It accurately delineates the mining sector in terms of extractive and exploration and development activities; b) It provides a symmetric treatment between produced and natural capital for the extraction activity, and c) It significantly improves the measurement of the real output of the exploration and development activity. Under this alternative framework, the mining sector’s multifactor productivity grew annually 3.8 % over the 1981-2000 period, compared to no productivity gain for the official figures. As a result, the mining sector now reports the second most rapid multifactor productivity growth of the entire business sector, after that of computers industry. While this revision is substantial, it still remains conservative since the deterioration in the quality of natural capital is not accounted for.
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.
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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.070 | 0.019 |
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".