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
Fair-division problems are ubiquitous.They range from the day-to-day chore assignments to the Israeli-Palestinian conflict and include the division of an inheritance to the heirs (Brams & Taylor, 1999;Massoud, 2000).Many intuitive and self-implementable algorithms guaranteeing "fairness" have been devised in the past 50 years (Brams & Taylor, 1996).So far, very few empirical studies have put them to the test (Daniel & Parco, 2005; Schneider & Krmer, 2004).In fact, it is not even known to what extent the solutions derived from these algorithms are satisfactory to human players.Here, we present an experiment that investigated the satisfaction of two pairs of players who divided 10 indivisible goods between themselves.A genetic algorithm was used to search for the best division candidates.Results show that some of the best divisions found by the genetic algorithm were rated as more mutually satisfactory than the ones derived from six typical fair-division algorithms.Analyses on temporal fluctuation and non-additivity of preferences could partially explain this result.Ideas for the future implementation of a more flexible and unconstrained approach are discussed.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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