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Record W3205017079 · doi:10.1177/23792981211054848

Sorting It Out: Identifying and Addressing Conflicts and Business Ethics in Global Value Networks

2021· article· en· W3205017079 on OpenAlexaff
Matthew C. Davis, Hinrich Voss, Mark Sumner, Divya Singhal

Bibliographic record

VenueManagement Teaching Review · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicHealthcare Facilities Design and Sustainability
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsCard sortingValue (mathematics)Task (project management)DilemmasortBusiness ethicsKnowledge managementCompromiseEthical dilemmaPublic relationsProduct (mathematics)SociologyEngineering ethicsComputer sciencePolitical scienceManagementEngineeringEpistemologySocial science

Abstract

fetched live from OpenAlex

Global value networks are often large, complex, and opaque. Understanding the relationships among stakeholders involved in these networks or organizations can be challenging. This card sort task provides an interactive way to engage participants in questioning the roles of stakeholders who are involved in a business ethics dilemma or an organizational product failure. This card sort task and discussion activity encourages participants to recognize that stakeholders may hold different knowledge, responsibility, or power; identify competing, conflicting, or complementary interests across stakeholders; articulate logical arguments; and engage in debate, compromise, and critical evaluation. This technique has been used successfully with undergraduate and postgraduate business, management, and social science students and is suitable for in-person and remote classes.

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)0.105
metaresearch head score (Gemma)0.122
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.105
Threshold uncertainty score0.556

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1050.122
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.005
Science and technology studies0.0110.018
Scholarly communication0.0110.020
Open science0.0030.010
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0050.001

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 head0.175
GPT teacher head0.448
Teacher spread0.274 · 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 designQualitative
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

Citations2
Published2021
Admission routes1
Has abstractyes

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