Bibliographic record
Abstract
Part A To illustrate the practical use of the critical-level utilitarian principles discussed in earlier chapters, we examine their use in two economic models. The first analyzes the problem of allocating a foreign-aid budget to different types of expenditures. In it, we assume that aid received by a developing country can be used to fund consumption or population control (prevention of births) and use a two-period model where population size in period two is determined by the amount spent on population control in period one. The second application examines the use of animals in research and food production. We evaluate various policies with a generalization of critical-level utilitarianism that takes account of the interests of non-human sentient animals and allows critical levels to differ across species. In both applications, population size is treated as a continuous variable to simplify the analysis. FOREIGN AID AND POPULATION POLICY Population policy is replete with ethical difficulties (Sen 1994, 1995) and policy decisions are complicated by imperfect knowledge of the effectiveness of policy options. Should we rely, for example, on the free choices of potential parents, improved education, or (possibly coercive) family-planning programs? These conundrums are made worse if there is disagreement about the ethical standards that should be used to evaluate possible outcomes. If policies are evaluated using average utilitarianism, for example, the result will be smaller populations than those recommended by classical utilitarianism.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.199 | 0.096 |
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".