Theory and conceptual frameworks: blame and credit/centralization and decentralization
Bibliographic record
Abstract
Abstract In this presentation we will introduce the politics of blame-avoidance and credit taking. We will also conceptualize centralization between and within governments and lay out the conceptual frameworks which have guided this research. It is almost axiomatic in political science that politicians seek credit and avoid blame. If there is something that will be popular, they try to take credit for it; and if there is something unpopular, they will try to avoid blame and, if possible, cast blame for it onto opponents. If good or bad outcomes cannot be traced to their actions, they will try to change the subject and opt for ‘position-taking' in which they declare their fidelity to what they see as popular positions. This was the strategy adopted by many politicians of the populist radical right around the world during the pandemic, though as the pandemic wore on the ones out of power increasingly focused on blaming incumbent governments for public health measures. 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 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).
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.008 | 0.100 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 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".