Bibliographic record
Abstract
ABSTRACT The conditions of strong Condorcet winner consistency and strong Condorcet loser consistency are, in essence, universally accepted as attractive criteria to evaluate the performance of social choice functions. However, there are many situations in which these conditions are silent because such winners and losers may not exist. Hence, weakening these desiderata to extend the domain of profiles where they apply is an appealing task. Yet, the often‐proposed and accepted weak counterparts of these properties suffer from the shortcoming that it is possible for all weak Condorcet winners to be weak Condorcet losers at the same time, thus leading to contradictory recommendations regarding their use as normative criteria. After arguing that this anomaly is pervasive, even in the presence of substantial and important domain restrictions, we propose to use intermediate notions of Condorcet‐type winners and losers that are between these two extremes: their associated consistency requirements share the intuitive appeal of strong Condorcet winner consistency and strong Condorcet loser consistency and avoid the contradictory recommendations that may derive from the double identification of candidates as being weak Condorcet winners and losers at the same time. We examine the extent to which our intermediate consistency conditions are compatible with various additional attractive normative criteria. Finally, we introduce a class of social choice functions that are consistent with the recommendations of our new proposals and can be extended to the universal domain through the lexicographical use of complementary choice criteria, in the spirit of previous approaches by noted authors like Pierre Daunou and Duncan Black.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 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".