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 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 | 0.015 | 0.099 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".