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Researching Memory Work and Identity Work

2024· article· en· W4400439317 on OpenAlexaff
Christina Hoon, Alina Baluch, William Foster, Trevor Lyle Israelsen, Peter Jaskiewicz, Evelyn Micelotta, Innan Sasaki, Hamid Foroughi, Jana Boevers, Diego M. Coraiola

Bibliographic record

VenueAcademy of Management Proceedings · 2024
Typearticle
Languageen
FieldPsychology
TopicMemory, Trauma, and Commemoration
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsWork (physics)Memory workIdentity (music)PsychologySociologyCognitive scienceEpistemologyEngineeringArtMechanical engineeringAestheticsPhilosophy

Abstract

fetched live from OpenAlex

Memory work (i.e. ‘how we (re)construct the past’) and identity work (‘who we think we are’) are deeply intertwined with collective remembering shaping and being shaped by the construction of individual and collective identities. This symposium seeks to advance organizational memory studies and explore the linkages of memory work to identity work as both of these forms of social symbolic work are important for understanding how members respond to the multi-faceted, concurrent challenges that organizations face. It aims to develop scholarship by exploring important new directions stemming from: 1) the opportunity to examine how memory work enriches the study of identity work (and vice versa); 2) the opportunity to explore memory work and identity work at different levels of analysis and their effects; and 3) the opportunity to explore the potential value of focusing on temporality for theorizing on memory work and identity work. In this symposium, we want to stimulate discussion about this connection and how scholars can better reimagine the organization from the inside out, i.e. the purposeful reflexive efforts that its members engage in when constructing and negotiating memories and identity, by unlocking a wave of insights from bringing together the study of remembering and being.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.632
Threshold uncertainty score0.605

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.058
GPT teacher head0.368
Teacher spread0.310 · 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 designObservational
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

Citations0
Published2024
Admission routes1
Has abstractyes

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