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Record W4385311294 · doi:10.1002/bse.3510

First things first: Unselfconscious corporate virtuosity and corporate performance

2023· article· en· W4385311294 on OpenAlexaff
Anthony M. Gould, Jean‐Etienne Joullié, Kate Gould

Bibliographic record

VenueBusiness Strategy and the Environment · 2023
Typearticle
Languageen
FieldDecision Sciences
TopicEthics in Business and Education
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsHonestyCorporate social responsibilityProxy (statistics)Corporate communicationAccountingStakeholderBusinessCorporate securityCorporate governanceSustainabilityCorporate sustainabilityPublic relationsPsychologyPolitical scienceFinanceSocial psychologyComputer science

Abstract

fetched live from OpenAlex

Abstract This article investigates the relationship between unselfconscious corporate virtuosity and corporate performance using a novel methodology. It contends that honesty in presenting CSR activities is a proxy for corporate virtuosity and that syntheticity of language indexes unselfconscious honesty in corporate reporting. The study's research question is: Is there evidence that being more corporately ethical in the absence of concern about being so perceived enhances hardcore measures of organisational performance? To answer this question, a linguistic analysis of sustainability reports of firms operating in the Mining, Crude Oil Production Industry is undertaken to reveal that firms' financial performance varies as a function of a particular kind of straightforward language use in corporate reporting. This exercise provides evidence that unselfconscious corporate virtuosity is associated with better corporate financial performance. Implications for practice of this finding are explored.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.335
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
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 head0.145
GPT teacher head0.286
Teacher spread0.141 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations3
Published2023
Admission routes1
Has abstractyes

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