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Record W2106284692 · doi:10.1111/joms.12056

Ethics in the Production and Dissemination of Management Research: Institutional Failure or Individual Fallibility?

2013· article· en· W2106284692 on OpenAlexaff
Benson Honig, Joseph Lampel, Donald S. Siegel, Paul L. Drnevich

Bibliographic record

VenueJournal of Management Studies · 2013
Typearticle
Languageen
FieldDecision Sciences
TopicEthics in Business and Education
Canadian institutionsMcMaster University
Fundersnot available
KeywordsScholarshipVariety (cybernetics)LegitimacyPromotion (chess)Competition (biology)Academic freedomPublic relationsNormativeSociologyPolitical scienceProduction (economics)Higher educationEconomicsLaw

Abstract

fetched live from OpenAlex

Abstract Over the past 50 years, we have witnessed considerable growth in business education, increased competition among business schools, and higher expectations for faculty scholarship. Increasing competition among scholars for limited publication opportunities in top‐tier journals and the proliferation of bottom‐tier journals has given rise to a variety of systemic ethical issues and dilemmas, for scholars and their institutions. In this article, we critically examine the current state of normative publishing activities and expectations, including doctoral education, promotion and tenure processes and research expectations, editorial and peer review processes, academic freedom, acceptable breadth, depth, and accuracy or legitimacy of research designs and methodologies, academic integrity, replication, and data availability concerning the trends and implications of contemporary and future management scholarship. We also provide recommendations for additional research and discussion on these issues.

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)0.440
metaresearch head score (Gemma)0.506
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.994
Threshold uncertainty score0.690

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.4400.506
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0080.088
Scholarly communication0.0280.020
Open science0.0040.017
Research integrity0.0060.010
Insufficient payload (model declined to judge)0.0030.001

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 head0.565
GPT teacher head0.541
Teacher spread0.024 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designTheoretical or conceptual
DomainIncentives
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

Citations77
Published2013
Admission routes1
Has abstractyes

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