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Record W3206856472 · doi:10.1007/s10551-021-04948-4

Markets Within the Limit of Feasibility

2021· article· en· W3206856472 on OpenAlexaff
Kenneth Silver

Bibliographic record

VenueJournal of Business Ethics · 2021
Typearticle
Languageen
FieldNeuroscience
TopicFree Will and Agency
Canadian institutionsTrinity College
FundersTrinity College DublinIrish Research eLibrary
KeywordsLeverage (statistics)EconomicsConstraint (computer-aided design)PoliticsBusiness ethicsRelevance (law)Law and economicsFree marketLimit (mathematics)Neoclassical economicsMicroeconomicsPositive economicsLawPolitical scienceComputer scienceManagement

Abstract

fetched live from OpenAlex

Abstract The ‘limits of markets’ debate broadly concerns the question of when it is (im)permissible to have a market in some good. Markets can be of tremendous benefit to society, but many have felt that certain goods should not be for sale (e.g., sex, kidneys, bombs). Their sale is argued to be corrupting, exploitative, or to express a form of disrespect. In Markets without Limits , Jason Brennan and Peter Jaworski have recently argued to the contrary: For any good, as long as it is permissible to give it for free, then it is permissible to give it for money. Their thesis has led to a number of engaging objections, and I leverage recent work on the nature of feasibility within political philosophy to offer a new challenge. I argue that feasibility offers a constraint on which markets can be permissibly implemented. Though it may be possible to create a morally acceptable version of an otherwise repugnant market, some of these markets may be infeasible, and so we are not permitted to implement them. After laying out this challenge, I consider several replies. They concern the relevance of feasibility, and whether any markets really are infeasible. This provides an opportunity to explore the dangers of pursuing the infeasible and with markets generally. I conclude by considering what might lead us to pursue these markets despite their infeasibility, or how knowledge of infeasibility may prove useful regardless.

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.

How this classification was reachedexpand

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)0.002
metaresearch head score (Gemma)0.016
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.316
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.193
GPT teacher head0.325
Teacher spread0.132 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2021
Admission routes1
Has abstractyes

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