Bibliographic record
Abstract
The economy depends on the essential nonrenewable resource and the path of extraction is nondecreasing and inefficient. At some point the government gradually switches to a sustainable (in sense of nondecreasing consumption over time) pattern of the resource use. Technical restrictions do not allow to switch to the efficient extraction instantly. Transition curves calibrated to the current pattern of world oil production are used as the extraction paths in the "intermediate" period. However, there is no solution in finite time for the "smooth" switching from the optimal "transition" to the optimal efficient path, constructed with respect to the same welfare criterion. We analyze numerically two approaches for the approximate solution: "epsilon-smooth" switching and "epsilon-optimal" transition curve with smooth switching. Both cases give the unexpected result: the consumption path along the "inefficient" transition curve is always superior to the constant which we obtain after switching to the "efficient" Hartwick's curve. The result implies that for the correct switching to the efficient curve in finite time the saving rule must be adjusted. We estimate the importance of following the efficient path by comparing the consumption along the plausible transition path and the efficient pattern of the resource use. For simplicity we use in our examples the constant per capita consumption as a welfare criterion and the Hartwick rule as the benchmark of investment rule.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".