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Organizational Fields as Mnemonic Communities

2017· article· en· W2765425433 on OpenAlexaff
Diego M. Coraiola, Roy Suddaby, William Foster

Bibliographic record

VenueAcademy of Management Proceedings · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMnemonicConstruct (python library)Meaning (existential)Bridging (networking)Field (mathematics)EpistemologyCognitionCognitive scienceCollective memoryPsychologySociologySocial psychologyCognitive psychologyComputer sciencePolitical scienceMathematicsPhilosophy

Abstract

fetched live from OpenAlex

The concept of an organizational field has become influential in management theory. Despite its prominence, the construct has defied precise definition. Most definitions emphasize either structural elements of fields (fields as place) or their ideational elements (fields as meaning systems). Missing from this analysis is an appreciation of how meaning is given to structural relations. Our core thesis is that memory is a critically important bridging construct through which meaning is given to place. We demonstrate that organizational fields are historical accretions of shared memories that are reproduced and become objectified over time until they acquire the status of ontological reality. We introduce the term mnemonic fields to capture the understanding that fields are cognitions of network relations that are created, maintained and changed through processes of collective memory.

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)0.002
metaresearch head score (Gemma)0.004
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: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
Science and technology studies0.0020.010
Scholarly communication0.0050.011
Open science0.0010.004
Research integrity0.0010.001
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 head0.027
GPT teacher head0.249
Teacher spread0.223 · 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

Citations7
Published2017
Admission routes1
Has abstractyes

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