Rhetoric of epistemic authority: Defending field positions during the financial crisis
Bibliographic record
Abstract
In this article we explore how elite actors respond to a field-wide crisis. Drawing from a study of CEOs of large US banks in the immediate aftermath of the global financial crisis, we show how elite actors use rhetorical strategies to defend their dominant position in the field. Specifically, we show how actors strengthen their epistemic authority – the perceived expertise and trustworthiness of an actor – through four distinct but interwoven rhetorical strategies. Actors used two internally-directed means of strengthening epistemic authority by providing rational guarantees and expressing normative responsibilities, and two externally-directed strategies that sought to strengthen their own epistemic authority by lowering the epistemic authority of others through critiquing judgments and questioning motives. We contribute to research on defensive institutional work by highlighting how elite actors rhetorically defended their position following a field-wide crisis.
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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 | 0.017 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.011 | 0.026 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".