Bibliographic record
Abstract
do not necessarily reflect views of the OECD or its Member countries. The paper benefitted from comments on an earlier version by participants at a workshop on Rethinking Total Factor Productivity Measurement, organised by Erwin Diewert and Alice Nakamura (October 2003, Ottawa). The author is also grateful to Mathilde Mas (University of Valencia and IVIE) for many useful discussions on the subject. This paper discusses the computation of capital services measures with user cost expressions that employ exogenous rates of return, as well as expected depreciation and expected asset price changes. One consequence of this formulation is that total capital remuneration does not necessarily equal non-labour income as given by the national accounts. The paper proposes several interpretations of this discrepancy, discusses implications for MFP measures and growth accounting and puts forward one preferred productivity measure. The methods are implemented empirically for four OECD countries.
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How this classification was reachedexpand
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) | 0.006 | 0.003 |
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; both teacher heads agree on what is shown here.
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".