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Organizational forgetting: Reviewing 40 years of research

2015· article· en· W2529453807 on OpenAlexaff
Stefania Mariano, Andrea Casey, Fernando Olivera

Bibliographic record

VenueAcademy of Management Proceedings · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicOrganizational Learning and Leadership
Canadian institutionsWestern University
Fundersnot available
KeywordsForgettingComprehensionPsychologySocialityOrganizational structureKnowledge managementOrganizational learningCognitionOrganizational theoryCognitive psychologySociologyComputer sciencePolitical scienceManagement

Abstract

fetched live from OpenAlex

We conducted a systematic review of the literature on organizational forgetting that has appeared in the past 40 years to assess unlearning, knowledge loss, and depreciation mechanisms. This review involved examining 64 key papers in organizational forgetting and related contributions to the broader literature of organizational learning. The analysis identified four critical themes that have shaped the current debate: (1) managerial involvement: influence and cognition; (2) feedback processes; (3) social interactions: structure, sociality, and inquiry culture; and (4) knowledge comprehension and memory. Taken as a whole, these themes allowed us to provide an organizing framework that can help integrate a wide array of quantitative and qualitative work on forgetting. Further, we identified four major areas that require further research: power relations; the interaction between structure and discourse; descriptions of factors, methods, phases, and measures of organizational forgetting; and links to benefits, hindrances, strategies, and performance.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.784
Threshold uncertainty score0.531

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.127
GPT teacher head0.322
Teacher spread0.195 · 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 teacher head, 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

Citations4
Published2015
Admission routes1
Has abstractyes

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