The language of leadership in a deadly pandemic
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose This paper explores the rhetorical and strategic nature of the language political and business leaders used during the early stages of the COVID-19 pandemic – or what we refer to as their pandemic-speak. Design/methodology/approach This paper draws opportunistically on examples of communications that have appeared variously in letters to shareholders, tweets, and public comments of CEOs of major companies, and in press briefings of several political leaders. Findings Good and bad examples of the exercise of leadership through pandemic-speak are presented. Some of the examples discussed are characterised variously by confected positivity, hubris, hyperbole, misinformation, recklessness, appeal to patriotic values, and abuse of the adjectives “unprecedented” and “extraordinary.” The term “COVID-19” is used as an ideographic “whipping post” to deflect attention from the implication of leaders in the inadequate level of preparedness for the pandemic. Originality/value This paper reinforces the point that the language of leaders is usually not innocent, but must be monitored closely to enhance meaning, transparency and accountability. CEOs should be less self-serving and instinctive in their crisis communications. Their language should reflect a caring attitude and encourage followers to have trust and confidence in them. Leaders should place greater stead in the wisdom offered in many studies of how to exercise strategic communication choices in a crisis.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it