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Record W4413842101 · doi:10.1177/01708406251370508

Enrolling Deceased Founders in Times of Change or Discontinuity: The evocative powers and perils of presentification

2025· article· en· W4413842101 on OpenAlexafffund
Nora Meziani, Viviane Sergi, Ann Langley, Joëlle Basque

Bibliographic record

VenueOrganization Studies · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFamily Business Performance and Succession
Canadian institutionsUniversité TÉLUQHEC MontréalUniversité du Québec à Montréal
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsDiscontinuity (linguistics)SociologyEconomic geographyPolitical scienceEconomicsPhilosophyLinguistics

Abstract

fetched live from OpenAlex

Scholars have given increasing attention to the uses of the past in organizational life, with several studies considering how founder figures may be drawn on in sensegiving to help promote organizational change. Yet most research has been one-sided, neglecting the possibility that leaders’ sensegiving attempts might be contested. Based on three contrasting case studies, we identify four modalities through which deceased founders may be presentified (i.e., made present, despite their physical absence) in leaders’ sensegiving attempts and we examine how and why such efforts may be authenticated or contested by others. Our study contributes by showing how deceased founders may not be powerful figures in themselves, but are made powerful by being presentified in evocative ways that reach beyond citing words to calling up emotions, memories, and vivid imagery. The study highlights how audiences develop complementary strategies to either oppose leaders (through counter-presentifications) or to support them (through amplifying presentifications), adding friction or fluidity to the communicative process. Finally, the study illuminates contextual facilitators that explain how actors’ relative positioning with respect to founders and audiences allows certain individuals to tap into more privileged memory sources to presentify founders in more evocative ways.

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)0.000
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 score0.038
Threshold uncertainty score0.191

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.072
GPT teacher head0.309
Teacher spread0.237 · 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
Published2025
Admission routes2
Has abstractyes

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