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Corruption as a Form of Misconduct: Clarifying Concepts and Charting Ways Forward

2022· article· en· W4286666434 on OpenAlexaff
Diana Dakhlallah, Christopher B. Yenkey, Brandy Aven, Donald Palmer, Erin Metz McDonnell

Bibliographic record

VenueAcademy of Management Proceedings · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicCorruption and Economic Development
Canadian institutionsMcGill University
Fundersnot available
KeywordsMisconductScholarshipPublic relationsLanguage changeContext (archaeology)Political sciencePsychological interventionOrganizational behaviorSociologyPsychologySocial psychologyLaw

Abstract

fetched live from OpenAlex

Corruption is one of the most pervasive threats to business environments and human rights around the world. Despite its prevalence and impact, management scholarship has yet to develop a mature field of research on the subject. The rapidly growing body of management research on organizational misconduct has begun to address corruption, but this growing sub-field is in need of both conceptual clarification as well as greater attention to the international organizational contexts where it is particularly problematic. This panel is designed to meet three goals: 1) demonstrate how scholarship on corruption makes distinct contributions to the large and growing literature on organizational misconduct; 2) demonstrate that to accurately characterize corruption dynamics, generate relevant theory, and design effective interventions, analyses are well-served by focusing attention on within and across organizational variation with deep attention to context; 3) build a more cohesive community of established and rising scholars whose research practice on corruption and organizational misconduct is characterized by deep engagement with organizational spaces, appropriately situated in their local field settings. We will address these issues with a combination of a panel discussion by experts and a facilitated Q&A session between panelists and the audience. Our panel includes presentations from organizational theorists who actively study corruption and organizational misconduct in both Western and non-Western contexts.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.769
Threshold uncertainty score0.608

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.057
GPT teacher head0.329
Teacher spread0.273 · 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 designNot applicable
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

Citations0
Published2022
Admission routes1
Has abstractyes

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