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Record W3115803351 · doi:10.1017/beq.2020.42

Where MLM Intersects MFA: Morally Suspect Goods and the Grounds for Regulatory Action

2020· article· en· W3115803351 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

VenueBusiness Ethics Quarterly · 2020
Typearticle
Languageen
FieldDecision Sciences
TopicEthics in Business and Education
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsSuspectNormativeEconomicsAction (physics)Business ethicsLaw and economicsMarket failurePreferenceMicroeconomicsPareto principlePositive economicsLawPolitical science

Abstract

fetched live from OpenAlex

The market failures approach (MFA) to business ethics argues that economic theory regarding the efficient workings of a market can generate normative prescriptions for managerial behaviour. It argues that actions that inhibit Pareto optimal solutions are immoral. However, the approach fails to identify goods that should be regulated or prohibited from the market, something common to the moral limits to markets (MLM) approach to business ethics. There are, however, numerous assumptions underlying Paretian efficiency, including some about the preferences of market participants. Trade in some goods violates some of these assumptions, and so these goods are morally suspect and can be understood to indicate that the market for these goods is not moral. This creates grounds sufficient for regulating, and possibly prohibiting, these goods. To help determine whether it is then necessary to regulate the goods, I propose a supplementary economic analysis to ascertain why an assumption regarding a particular preference is being violated.

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)0.007
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.717
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.328
GPT teacher head0.424
Teacher spread0.096 · 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