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Record W4388850648 · doi:10.1111/joms.13022

Multi‐Temporality and the Ghostly: How Communing with Times Past Informs Organizational Futures

2023· article· en· W4388850648 on OpenAlexaff
Mairi Maclean, Charles Harvey, Roy Suddaby, Diego M. Coraiola

Bibliographic record

VenueJournal of Management Studies · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsTemporalityPerformative utteranceDialogical selfDialecticFutures contractSociologyAction (physics)EpistemologyTemporalitiesAestheticsPolitical scienceEconomicsArtPhilosophyLaw

Abstract

fetched live from OpenAlex

Abstract Despite growing interest in time, history, and memory, we lack an understanding of the multi‐temporal reality of organizations – how past, present, and future intersect to inform organizational life. In assuming that legacies are bequeathed from past to present, there has been little theorization on how this works practically. We propose that the lexicon of the ghostly can help. We contribute a theory of ghostly influence from past to future by offering a framework focusing on core moments of organizational existence: foundation, strategic change, and longevity commemoration, and illustrate this using a case study of consumer goods multinational Procter & Gamble (1930–2010). In showing that organizational ghosts, absent members whose presence is consequential to the actions of living members, are active and dialogical, we illuminate a dialectical interaction missing from other non‐linear conceptions of temporality. This emphasizes the performative force of a dynamic past that provides an inference to action in the present and future.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.466
Threshold uncertainty score0.749

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 studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.023
GPT teacher head0.239
Teacher spread0.216 · 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

Citations17
Published2023
Admission routes1
Has abstractyes

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