Bibliographic record
Abstract
Ibn Ezra (Sefar ha-Mispar (The Book of the Number, in Hebrew), Verona (German trans: Silberberg M. (1895)). Kauffmann, Frankfurt am Main, 1146), Rabinovitch (Probability and statistical inference in medieval Jewish literature. University of Toronto Press, Toronto, 1973) and O'Neill (Math Soc Sci 2(4):345–371, 1982)proposed a method for solving the "rights arbitration problem" (one of the historical problems of "bankruptcy") for n claimants when the estate E is equal to the largest claim. However, when the greatest claim is for less than the estate, the question of what to do with the difference between E and the largest claim is posed. Alcalde et al.'s (Econ Theory 26(1):103–114, 2005) Generalized Ibn Ezra Value (GiEV), solves the problem in T iterations, of n steps. By using Monte-Carlo experiments, we show that: (i) T grows linearly with the number of claimants, which makes GiEV rapidly impracticable for real applications. (ii) The more E is close to the total claim d, themore T grows: T linearly grows when E exponentially approaches d by a factor 10. Moreover, we proved through theory that GiEV fails to provide a solution in a finite number of iterations for the trivial case E = d, whereas it should obviously find a solution in one iteration. So, even if GiEV is convergent, the sum of claims d appears as an asymptote: the number of iterations tends to infinite when the estate E approaches the claims total d. We conclude that GiEV is inefficient and usable only when: (1) the number of claimants is low, and (2) the estate E is largely lower than the total claims d.
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How this classification was reachedexpand
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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".