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Being, Doing and Becoming: Organizational Identity Work and the Process of Managing Exogenous Shocks

2019· article· en· W2965786255 on OpenAlexaff
François Bastien, William Foster

Bibliographic record

VenueAcademy of Management Proceedings · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsOrganizational identityIdentity (music)Work (physics)Public relationsProcess (computing)PerceptionOrganizational studiesSample (material)Organizational learningSociologyShock (circulatory)Organizational commitmentBusinessPolitical sciencePsychologyKnowledge managementComputer scienceEngineering

Abstract

fetched live from OpenAlex

Organizational studies struggle to explain the processes through which organizational identities are created and re-created through time. The main reasons for this rests in the disagreements about the manner in which the identity of organizations has been theorized. That is, organizational identity has been limited by theoretical approaches that are built on individual perceptions about a specific time in a specific place. This paper, instead, focuses on how exogenous organizational shocks initiate processes of identity work. Our empirical site is a sample of bars and restaurants that were affected by the implementation of anti-smoking laws. Based on eight (8) distinct case studies, we argue that different types of organizational identities beget corresponding identity work. Our paper links organizational identity to identity work, in terms of how ‘being’ informs ‘doing’ and ‘becoming’ for organizations managing an important exogenous shock.

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)0.008
metaresearch head score (Gemma)0.014
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 score0.010
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.022
Scholarly communication0.0100.009
Open science0.0010.007
Research integrity0.0030.003
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 head0.009
GPT teacher head0.221
Teacher spread0.212 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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