Bibliographic record
Abstract
Part A In this chapter, we introduce population principles and explore their properties. Because the principles that we consider are both welfarist and anonymous, each one can be defined by a single anonymous ordering of utility vectors (see Chapter 3). We follow standard practice and normalize utilities so that a utility level of zero represents neutrality. Most population principles have value functions that represent their social evaluation orderings. If a value function exists, utility vector u is at least as good as utility vector v if and only if the value associated with u is no less than the value associated with v . Our investigation uses same-number extensions of fixed-population axioms such as Pareto weak preference and continuity and additional axioms that apply to population principles alone. We prove, in Chapter 6, that there is no population principle that satisfies all our axioms. It is possible, however, to find ethically attractive population principles that satisfy some of them. We present and examine the critical-level generalized utilitarian class, the restricted critical-level generalized utilitarian class, the number-sensitive critical level generalized utilitarian class, the restricted number-sensitive critical-level generalized utilitarian class, the number-dampened generalized utilitarian class (Ng 1986), and the restricted number-dampened generalized utilitarian class (Hurka 2000). All these principles rank alternatives with the same population size using generalized utilitarianism. In addition, each class contains a subclass whose members rank same-number alternatives with utilitarianism. In addition to the above classes of principles, we consider variable-population extensions of maximin and leximin as well as classes of principles that have been suggested by Carlson (1998) and Sider (1991).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".