6.N. Workshop: The politics of credit and blame: centralizing/decentralizing governance in the COVID-19 pandemic
Bibliographic record
Abstract
Abstract Objective The overarching aim of this workshop is to use the theory of credit and blame politics to explain significant and dynamic shifts in power relations through centralization/decentralization within and between governments during the pandemic. Centralization is not just territorial but can also apply within government and change the extend of autonomy and influence ministers, departments and agencies have. To this end we will have a total of five presentations: Introduction into the theory of politics of blame and credit as a driver for centralization/decentralization; three country case studies including Austria, the Czech Republic and France and a concluding presentation on Credit-taking, blame-avoidance as drivers for power shifts: implications for effective emergency responses Background Centralization between governments means an increase in the power of the central government vis-a-vis other ‘subnational' governments such as regions, states, provinces, or municipalities. ‘Command and control' is a common recommendation in public health emergencies (3) and central governments do often take powers over or away from subnational governments in crises. This is most politically contentious in federal states such Spain, Canada, or Germany, but can happen even in countries where there is a history of only local government (such as Ireland, Portugal, or the Nordic states). This workshop is built on a forthcoming publication. To ensure interactivity, the chair will feed back comments to the speakers between presentations and before the end of the session. Key messages In COVID-19 responses credit-taking and blame-avoidance leads to major powershifts within and between governments. Emergency response planning, knowledge broker, media and civil society need to help mitigate the negative effects of credit-taking and blame avoidance for a better pandemic response.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.034 | 0.008 |
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".