Bibliographic record
Abstract
This paper is a contribution to the understanding and development of social discounting regimes. It first addresses three, often overlooked implications of how public funding differs from private financing by debt and equity. One implication is that the cost of systematic (income-correlated) risk in public service benefits does not fall as a rate of return, but as an absolute reduction in the value of the benefits. This is quantitatively important. Another is that, while ‘social opportunity cost’ discounting can for some governments be the best practicable option for most cost benefit analysis, it is unsuitable for other applications, which require lower rates. This can be handled by a hybrid regime. Third, with ‘social time preference’ discounting it is usually assumed that the cost of public funding should be handled by an explicit shadow price (≥1) for public spending. However a value-for-money approach, optimising spending from given, constrained budgets, is in important ways superior. The paper then examines US Federal and United Kingdom central government conventions, illustrating hybrid and value-for-money regimes, and also illustrating the difficulties of establishing and maintaining analytically rigorous social discounting procedures in practice.
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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 | 0.032 | 0.180 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".