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Record W2943924890 · doi:10.1177/0170840619844284

Those who control the past control the future: The dark side of rhetorical history

2019· article· en· W2943924890 on OpenAlexafffund
Brad Aeon, Kai Lamertz

Bibliographic record

VenueOrganization Studies · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsAthabasca UniversityConcordia University
FundersUniversity of TorontoConcordia UniversityAthabasca University
KeywordsRhetorical questionNarrativeDemiseSociologyCraftGloryExploitOrganizational identityFraming (construction)AestheticsPublic relationsPolitical scienceLawHistorySocial scienceLiterature

Abstract

fetched live from OpenAlex

What is the impact of rhetorical history on employees? We address this question by interviewing 29 workers in two organizations. Our critical interpretation of the findings suggests that managers willfully craft historical narratives to regulate workers’ identity and, ultimately, advance the organization’s agenda. Managers achieve this by peddling historical narratives that instill certain logics in workers. These logics, in turn, influence workers’ identification and involvement with the organization. The first logic, reflected glory, exploits the idea that what is historical is prestigious and coaxes workers into basking in their organization’s historical glory through identification. The second logic, preservation, exploits the idea that what is historical must be preserved and urges workers to be involved in their work to stave off the demise of the organization’s legacy. We further contend that organizations reinforce these logics using narrative resources—concepts that lend historical narratives more persuasiveness—such as place or longevity. Nevertheless, workers do not remain passive. While some engage in traditional resistance tactics, others leverage their collective memory as a counter-narrative to the organization’s narratives. While much has been said about the strategic uses of rhetorical history, we conclude by discussing its limitations and hitherto overlooked moral implications.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.828
Threshold uncertainty score0.543

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 communication0.0000.000
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 head0.014
GPT teacher head0.212
Teacher spread0.198 · 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 designNot applicable
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

Citations37
Published2019
Admission routes2
Has abstractyes

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