Blame avoidance and credit-claiming dynamics in government policy communications: evidence from leadership tweets in four OECD countries during the 2020–2022 COVID-19 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
Abstract Government information activities are often thought to be motivated by a classic calculus of blame minimization and credit maximization. However, the precise interactions of “blame” and “credit” communication activities in government are not well understood, and questions abound about how they are deployed in practice. This paper uses Natural Language Processing (NLP) machine-learning sentiment analysis of a unique dataset composed of several thousand tweets of high-level political leaders in four OECD countries—namely the Prime Ministers of the United Kingdom, Ireland, Australia, and Canada—during 2020–2022 to examine the relationships existing between “blame” and “credit” communication strategies and their relation to the changing severity of the COVID-19 pandemic, both in an objective and subjective sense. In general, the study suggests that during this high-impact, long-lasting, and waxing and waning crisis, political leaders acted in accordance with theoretical expectations when it came to communicating credit seeking messages during the periods when the COVID situation was thought to be improving, but they did not exclusively rely upon communicating blame or scapegoating when the situation was considered to be deteriorating. The consequences of this finding for blame and credit-based theories of government communication are then discussed.
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.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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