The Dogs That Did Not Bark
Bibliographic record
Abstract
This chapter turns to the comparison of cases. By analyzing the discontented cases, a clear pattern emerges. The positive cases share few characteristics save one: democratic discontent that arose when sharp economic contractions intensified the imperfections and contradictions of the political status quo. This argument is made using paired comparisons of the positive and negative cases (Canada with the USA/UK, Portugal with Spain, Uruguay with Brazil/Chile) to evaluate competing explanations. The second section of the chapter analyzes how discontent was avoided during the Great Recession by looking for shared features of the three negative cases. It finds that escaping the initial pain of a crisis was not a necessary condition for avoiding discontent. Instead, the key to maintaining democratic legitimacy lay in the political response to the crises, and in the adaptability and health of left-wing parties. In all three negative cases, center-left parties recognized crises as indictments of neoliberalism, rejected its calls for austerity. By responding to popular demands for help in difficult times, these parties deprived cultural conflicts of the oxygen needed for them to rage and avoided major upsurges of discontent.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".