Everybody to Count for One? Inclusion and Exclusion in Welfare-Consequentialist Public Policy
Bibliographic record
Abstract
Abstract Which individuals should count in a welfare-consequentialist analysis of public policy? Some answers to this question are parochial, and others are more inclusive. The most inclusive possible answer is ‘everybody to count for one.’ In other words, all individuals who are capable of having welfare – including foreigners, the unborn, and non-human animals – should be weighed equally. This article argues that ‘who should count’ is a question that requires a two-level answer. On the first level, a specification of welfare-consequentialism serves as an ethical ideal, a claim about the attributes that the ideal policy would have. ‘Everybody to count for one’ might succeed on this level. However, on the second level is the welfare-consequentialist analysis procedure used by human analysts to give advice on real policy questions. For epistemic reasons, the analysis procedure should be more parochial than ‘everybody to count for one.’
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.041 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.040 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".