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Record W3158217756 · doi:10.1177/10596011211009392

Brewing a Craft Impression: A Multilevel Study About the Orchestration of Organizational Impression Management Through Authenticity

2021· article· en· W3158217756 on OpenAlexaffabout
Kai Lamertz

Bibliographic record

VenueGroup & Organization Management · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsAthabasca University
Fundersnot available
KeywordsImpression managementIdentity (music)CraftOrganizational identityImpression formationScripting languagePublic relationsPsychologySociologyFace (sociological concept)Social psychologyAestheticsPolitical scienceOrganizational commitmentPerceptionComputer scienceSocial perception

Abstract

fetched live from OpenAlex

Organizations face the tricky challenge of portraying a coherent appearance of identity to make an authentic impression on their audiences. This study argues that authenticity is a cross-level mechanism through which organizations and their individual members orchestrate a coherent impression. A qualitative investigation revealed how five Canadian craft breweries claimed authenticity in their formal images by referencing a collective organizational identity in the environment. These claims were authenticated by external audiences and by individual organizational members. Two distinctly organizational forms of authentication expressed by individuals were (1) consensus about facets of organizational identity that mirrored the institutional authenticity claim as a party line and (2) role claims about leaders and employees, each comprising behavior scripts that corroborated the organization’s authenticity claim. The two forms of authentication also supported authentic leadership in the organization and contributed to an orchestrated appearance by linking institutional and individual impression management.

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)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.320
Threshold uncertainty score0.637

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0080.010
Scholarly communication0.0050.003
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.020
GPT teacher head0.240
Teacher spread0.220 · 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 designObservational
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

Citations8
Published2021
Admission routes2
Has abstractyes

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