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Record W3110081134 · doi:10.1177/0170840621989004

Duality and Social Position: Role expectations of people who combine outsider-ness and insider-ness in organizational change

2021· article· en· W3110081134 on OpenAlexaffabout
Amit Nigam, Esther Sackett, Brian Golden

Bibliographic record

VenueOrganization Studies · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsInsiderPosition (finance)Perspective (graphical)SociologyDuality (order theory)Social psychologySymbolic interactionismSocial changeEpistemologyPublic relationsPsychologyPolitical scienceEconomicsLaw

Abstract

fetched live from OpenAlex

A person’s social position shapes whether and how they can influence organizational change. While prior research establishes people whose social position combines outsider-ness and insider-ness as important change agents, we know little about how they influence change. We analyse a peer coaching initiative in Canadian hospitals to explain how outsider-insiders – in this case, organizational outsiders with professional proximity – advance change. Peer coaches were able to influence change by establishing and enacting a dual outsider-insider role and associated role expectations. We advance theory by showing that role expectations emphasizing duality that are rooted in social position, but created through social interaction, are a key mechanism by which the potential of outsider-insider social positions can be activated and mobilized to influence change. We advance theory on social position generally by highlighting the potential for integrating a symbolic interactionist perspective – focused on role expectations – into Bourdieu’s theory of fields.

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)0.006
metaresearch head score (Gemma)0.012
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0070.013
Scholarly communication0.0070.004
Open science0.0010.006
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.031
GPT teacher head0.253
Teacher spread0.222 · 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

Citations31
Published2021
Admission routes2
Has abstractyes

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