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Record W2068727666 · doi:10.1177/0951629807080774

Justice Preferences and the Arrow Problem

2007· article· en· W2068727666 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Theoretical Politics · 2007
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGame Theory and Voting Systems
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsSocial choice theorySocial welfare functionArrowNormativeEconomicsCondorcet methodRedistribution (election)Majority ruleVotingFunction (biology)WelfareMicroeconomicsMathematical economicsComputer sciencePolitical science

Abstract

fetched live from OpenAlex

Arrow showed that there is no general way to aggregate non-interpersonally comparable preferences or welfare into either a sensible social choice or a social welfare measure. With majority rule the problem manifests itself as voting cycles. The standard response to this problem has been developing `spatial models' built on restricted preferences (or welfare). We develop an alternative family of solutions. By assuming a culturally accepted conception of justice within a utility function, we establish the possibility of sensible aggregate choice implementable via majority rule. Various assumptions regarding the form the utility function are discussed. Conditions for a Condorcet winner in a problem of pure redistribution are derived for a number of models. Some of the implications of this perspective for the theory of democracy are considered. Developing a normatively interesting social welfare function may require introducing normative concerns into the preferences of the individuals rather than just into the properties of the aggregation system.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.200
Threshold uncertainty score0.319

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.032
GPT teacher head0.257
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it