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.
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 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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.025 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".