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Record W2995779140 · doi:10.1177/1350507619889737

From organizational learning to organizational mnemonics: Redrawing the boundaries of the field

2019· article· en· W2995779140 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueManagement Learning · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMnemonicScholarshipSociologyOrganizational studiesOrganizational learningOrganization studiesField (mathematics)EpistemologyOrganization developmentOrganizational theoryKnowledge managementPsychologyPolitical sciencePublic relationsManagementComputer scienceCognitive psychologyPhilosophy

Abstract

fetched live from OpenAlex

In this article, we advocate for a more balanced approach to the study of the past in management and organization studies. We define organizational mnemonics as a broader field of inquiry focused on theorizing the past as an integral part of organizational life, including three major epistemic communities—that is, functionalist, interpretive, and critical. We contend that much of organizational mnemonics research has been dominated by functionalism, at the expense of other approaches. To remediate this situation, we first characterize organizational mnemonics’ core epistemic communities. Second, we look at the boundary work at the interstices of these communities to explore possibilities of dialogue among them. We argue that the future of the study of the past in organizations should acknowledge different perspectives, the intersections among them, and make a conscientious effort to maintain diversity of scholarship in the field.

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.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.816
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.004
GPT teacher head0.188
Teacher spread0.184 · 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