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Record W3095975385 · doi:10.1177/0170840620974338

Organizational Memory Studies

2020· article· en· W3095975385 on OpenAlexaff
Hamid Foroughi, Diego M. Coraiola, Jukka Rintamäki, Sébastien Mena, William Foster

Bibliographic record

VenueOrganization Studies · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsOptimal distinctiveness theoryOrganization studiesForgettingPerspective (graphical)Field (mathematics)Performative utteranceSociologyOrganizational memoryEpistemologyOrganizational studiesOrganizational theoryCognitive scienceOrganization developmentKnowledge managementOrganizational learningPsychologyManagementComputer scienceCognitive psychologySocial psychology

Abstract

fetched live from OpenAlex

This paper provides an overview and discussion of the rapidly growing literature on organizational memory studies (OMS). We define OMS as an inquiry into the ways that remembering and forgetting shape, and are shaped by, organizations and organizing processes. The contribution of this article is threefold. We briefly review what we understand by organizational memory and explore some key debates and points of contestation in the field. Second, we identify four different perspectives that have been developed in OMS (functional, interpretive, critical and performative) and expand upon each perspective by showcasing articles published over the past decade. In particular, we examine four papers previously published in Organization Studies to show the distinctiveness of each perspective. Finally, we identify a number of areas for future research to facilitate the future development of OMS.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.006
Science and technology studies0.0030.004
Scholarly communication0.0050.007
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.050
GPT teacher head0.251
Teacher spread0.201 · 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
GenreReview

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

Citations88
Published2020
Admission routes1
Has abstractyes

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