Bibliographic record
Abstract
Distinguishing between national income and expenditure helps to shed light on some issues in green national accounting, including capital gains. Although their total is the same, different types of depreciation should be defined differently in the income and expenditure accounts. For example, there are two ways to define the depletion of non‐renewable resources. If depletion is defined as the resource rent, the unit value of the resource stock exceeds the current rent. If resource rent is viewed in terms of the resource’s contribution to national income, the stock can be valued at the current rent but depletion is less than resource rent. JEL classification: E20, Q30 Revenus et dépenses nationaux verts. Le fait de distinguer le revenu national et la dépense nationale aide àéclairer certains problèmes dans la comptabilité nationale verte, y compris en ce qui a trait aux gains de capitaux. Même si leur total est le même, différents types d’amortissement devraient être utilisés dans les comptes de revenus et de dépenses. Par exemple, il y a deux manières de définir l’épuisement des ressources non‐renouvelables. Si l’épuisement est défini par la rente sur la ressource, alors la valeur unitaire du stock de ressource est plus grande que la rente courante. Si la rente de la ressource est considérée comme la contribution de la ressource au revenu national, le stock peut être évalué au niveau de la rente courante, mais alors l’épuisement est moins que la rente de la ressource.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 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; 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".