Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This chapter examines the problem of evaluating policies with either risky or uncertain consequences. Under risk, the probabilities are known and agreed, whereas under uncertainty, probabilities are subjective and subject to disagreement. In the case of risk, Harsanyi’s Social Aggregation Theorem derives a representation of social utility as a weighted sum of individual utilities. But when there is uncertainty, two different and conflicting evaluation criteria, that is, ex ante and ex post, become available. The chapter explores this problem and sketches solutions. Similarly, when equality becomes the guiding question, there is a tension between ex ante and ex post evaluations, each leading to a conceptual loss, and the chapter explores solutions given to this further problem. It also covers Harsanyi’s Impartial Observer Theorem and its recent developments, and discusses the question raised by Sen of whether the weighted sum of individual utilities obtained by Harsanyi makes genuine utilitarian sense.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it