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Record W4409462547 · doi:10.1177/08933189251334822

Navigating Complexity: A Forum on Communication Research in High Reliability Organizations

2025· article· en· W4409462547 on OpenAlexaff
Jessica L. Ford, Rebecca M. Rice, Ryan S. Bisel, Stéphanie Fox, Trevor N Howard, William T. Howe, Kirstie McAllum, Arden C. Roeder, Amber Lynn Scott, Elizabeth A. Williams

Bibliographic record

VenueManagement Communication Quarterly · 2025
Typearticle
Languageen
FieldDecision Sciences
TopicComplex Systems and Decision Making
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsReliability (semiconductor)Organizational communicationKnowledge managementPublic relationsPsychologyComputer scienceSociologyBusinessPolitical science

Abstract

fetched live from OpenAlex

This forum joins communication researchers who explore high reliability organizations (HROs) and teams to discuss the communicative foundations of HRO. We argue that organizational communication researchers can make meaningful contributions to present challenges in HRO research. These challenges include (1) HRO’s focus on organizations versus processes of organizing, (2) tensions in defining success and failure to create reliability, and (3) the privileging of rational thought patterns and communication over emotional and care-centered communication. The authors in this forum push boundaries around organization types, consider overlooked and obscured knowledge, and question the role of power and materiality in HROs. Together, the forum advocates for the unique contributions organizational communication scholars can make to the study of HRO and, just as significantly, what HRO theorizing can contribute to organizational communication.

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)0.055
metaresearch head score (Gemma)0.067
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.055
Threshold uncertainty score0.293

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0550.067
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0060.004
Science and technology studies0.0210.020
Scholarly communication0.0300.043
Open science0.0040.027
Research integrity0.0310.024
Insufficient payload (model declined to judge)0.0110.001

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 head0.185
GPT teacher head0.478
Teacher spread0.293 · 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
GenreCommentary

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

Citations4
Published2025
Admission routes1
Has abstractyes

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