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Record W3207040187 · doi:10.33423/ajm.v20i5.3865

Linguistics as Metaphor in Organizational Regularization and Decay

2020· article· en· W3207040187 on OpenAlexaff
Marc S. Mentzer

Bibliographic record

VenueAmerican Journal of Management · 2020
Typearticle
Languageen
FieldArts and Humanities
Topiclinguistics and terminology studies
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMetaphorRegularization (linguistics)Cognitive linguisticsCognitionLinguisticsComputer scienceCognitive sciencePsychologyCognitive psychologySociologyPhilosophyNeuroscience

Abstract

fetched live from OpenAlex

Linguists have found that the twin tendencies of regularization and decay lead to the development of language structures that are simultaneously complex and unstable. This serves as a metaphor to illuminate the parallel processes of regularization and decay in organizational structure. Both the evolution of languages and the evolution of organizational structures reflect the desire to minimize cognitive work, as shown in the embracing of cognitive order while simultaneously seeking the course of least resistance.

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)0.007
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0050.062
Scholarly communication0.0090.017
Open science0.0010.007
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0030.001

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 head0.018
GPT teacher head0.226
Teacher spread0.208 · 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 designTheoretical or conceptual
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
Published2020
Admission routes1
Has abstractyes

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