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Record W4297830295 · doi:10.1177/01492063221117120

Organizational Social Relations and Social Embedding: A Pluralistic Review

2022· review· en· W4297830295 on OpenAlexafffund
Audrey-Anne Cyr, Isabelle Le Breton‐Miller, Danny Miller

Bibliographic record

VenueJournal of Management · 2022
Typereview
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsHEC Montréal
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsVariety (cybernetics)SociologyOrganization studiesOrganizational studiesEpistemologySocial relationKnowledge managementSocial psychologySocial sciencePsychologyOrganizational learningComputer science

Abstract

fetched live from OpenAlex

To date there has been little systematic organization of the extensive literature on the processes and mechanisms shaping social relationships in and around organizations. In an analysis of 372 studies from this literature, we identified a broad spectrum of assumptions, priorities, and relational issues emerging from multiple disciplines and theoretical lenses. Three dominant perspectives surfaced in our study: economic, organizational, and interactionist. Each manifests distinctive ontologies of social relations, actors, relational processes, and modes of social embedding. The rich variety of relationships and causal patterns discovered characterizes more fully these perspectives, suggesting opportunities for further research within each, and a wider range of conceptual options to target relational paradigms toward different types of organizations, problems, and levels of analysis. It also brings to light the pluralistic nature of social relations in organizational contexts and the processes by which they become embedded.

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)0.005
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.014
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0140.018
Science and technology studies0.0010.003
Scholarly communication0.0050.005
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.050
GPT teacher head0.300
Teacher spread0.250 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations18
Published2022
Admission routes2
Has abstractyes

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