Benefit versus Numbers versus Helping the Worst-off: An Alternative to the Prevalent Approach to the Just Distribution of Resources
Bibliographic record
Abstract
A central strand in philosophical debate over the just distribution of resources attempts to juggle three competing imperatives: helping those who are worst off, helping those who will benefit the most, and then – beyond this – determining when to aggregate such ‘worst off’ and ‘benefit’ claims, and when instead to treat no such claim as greater than that which any individual by herself can exert. Yet as various philosophers have observed, ‘we have no satisfactory theoretical characterization’ as to how to weigh each of the three imperatives against one another, we find it ‘difficult to state . . . precise or comprehensive conclusions’, and we do not yet have a ‘metric for integrating the three measures’. In what follows, I offer an approach to weighing the three criteria against one another that yields resolutions – in Hard Cases of the ‘saving one infant's life versus replacing ten elderly people's hips’ sort – that are cardinally definitive, intuitively satisfactory and theoretically justified.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.064 |
| Scholarly communication | 0.011 | 0.020 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".