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Record W2520850800 · doi:10.1017/beq.2016.45

Professionalism, Agency, and Market Failures

2016· article· en· W2520850800 on OpenAlexaff
Hasko von Kriegstein

Bibliographic record

VenueBusiness Ethics Quarterly · 2016
Typearticle
Languageen
FieldDecision Sciences
TopicEthics in Business and Education
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsDutyAgency (philosophy)BusinessMarket failureShareholderIdeal (ethics)Business ethicsLaw and economicsState (computer science)Industrial organizationEconomicsMicroeconomicsCorporate governanceFinanceLawManagementPolitical science

Abstract

fetched live from OpenAlex

ABSTRACT: According to the market failures approach to business ethics, beyond-compliance duties can be derived by employing the same rationale and arguments that justify state regulation of economic conduct. Very roughly, the idea is that managers have a duty to behave as if they were complying with an ideal regulatory regime ensuring Pareto-optimal market outcomes. Proponents of the approach argue that managers have a professional duty not to undermine the institutional setting that defines their role, namely the competitive market. This answer is inadequate, however, for it is the hierarchical firm, rather than the competitive market, that defines the role of corporate managers and shapes their professional obligations. Thus, if the obligations that the market failures approach generates are to apply to managers, they must do so in an indirect way. I suggest that the obligations the market failures approach generates directly apply to shareholders. Managers, in turn, inherit these obligations as part of their duties as loyal agents.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.010
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.023
Scholarly communication0.0040.003
Open science0.0010.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.280
GPT teacher head0.440
Teacher spread0.160 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations23
Published2016
Admission routes1
Has abstractyes

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