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Record W2071073404 · doi:10.1002/pa.179

Social networks and non‐market strategy

2004· article· en· W2071073404 on OpenAlexaff
John F. Mahon, Pursey Heugens, Kai Lamertz

Bibliographic record

VenueJournal of Public Affairs · 2004
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBusiness Strategy and Innovation
Canadian institutionsConcordia University
Fundersnot available
KeywordsStakeholderField (mathematics)Perspective (graphical)Set (abstract data type)Strategic managementSocial network analysisSocial network (sociolinguistics)Market analysisManagement scienceBusinessIndustrial organizationMarketingEconomicsComputer scienceSocial capitalManagementSociologySocial mediaSocial scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract To date, the field of non‐market strategy has little to offer in the way of an integrated perspective on the simultaneous management of strategic issues and corporate stakeholders. This paper employs social network analysis to make a number of theoretically grounded conjectures about the delicate relationships between stakeholder behaviour and issue evolution. It is found that social network analysis has the potential to enrich and integrate theoretical perspectives in the field of non‐market strategy, offering solutions to a set of previously unresolved puzzles. Copyright © 2004 Henry Stewart Publications

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.704
Threshold uncertainty score0.478

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.003
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 head0.025
GPT teacher head0.235
Teacher spread0.210 · 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 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

Citations1
Published2004
Admission routes1
Has abstractyes

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