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Record W4319934209 · doi:10.5465/annals.2021.0088

Ecologies of Memories: Memory Work Within and Between Organizations and Communities

2023· article· en· W4319934209 on OpenAlexaff
Diego M. Coraiola, William Foster, Sébastien Mena, Hamid Foroughi, Jukka Rintamäki

Bibliographic record

VenueAcademy of Management Annals · 2023
Typearticle
Languageen
FieldPsychology
TopicMemory, Trauma, and Commemoration
Canadian institutionsUniversity of VictoriaUniversity of Alberta
Fundersnot available
KeywordsWork (physics)SociologyMemory workPublic relationsPsychologyBusinessManagementPolitical scienceEpistemologyEconomicsEngineering

Abstract

fetched live from OpenAlex

In this paper, we review and synthesize the growing sociology-informed literature on organizational memory studies, which focuses on collective memory as a social construction of the past. To organize this literature, we present an ecological view of collective memory. Organizations, from this perspective, are conceived of as both constituted by a variety of mnemonic communities and, simultaneously, part of a broader ecology of mnemonic communities. We use this framework to guide our review of the various forms of memory work within and between mnemonic communities. Our review shows that much of the sociologically informed research has focused on memory work within communities. We also identify an emerging interest in the study of memory work between communities. In conclusion, we discuss possible future directions and outline a three-point agenda for future research that calls for a better understanding of the relational dynamics of memory with a focus on the organizing of memory, the embeddedness of memory work, and the construction of experiences of the past.

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)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.004
Science and technology studies0.0040.017
Scholarly communication0.0080.015
Open science0.0010.007
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.112
GPT teacher head0.360
Teacher spread0.248 · 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 designQualitative
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

Citations49
Published2023
Admission routes1
Has abstractyes

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