Those who control the past control the future: The dark side of rhetorical history
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.033 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".