Political Learning as a Catalyst of Moderation: Lessons from Democratic Consolidation in Greece
Bibliographic record
Abstract
Utilizing over 100 interviews conducted with Greek political and military elites, this article offers a refinement of the process of political learning, believed to contribute to democratic consolidation by modifying individuals' beliefs about political goals and the best means to achieve them. Using the Greek case as an empirical test, this study confirms the democratization literature's claim that elites learn from singular catastrophic events. It offers a refinement, however, of specific lessons and the related behavioural change. Moving beyond the main conclusions of that literature, the article argues that learning can arise in a variety of ways and from varied experiences. Inductive trial-and-error learning stimulated by success can also play a key role as can slow and cumulative learning, which results from the accumulation of both positive and negative lessons, and can proceed in a two-step process of instrumental learning first, followed by more principled learning later. Learning thus sometimes takes a tangled course: elites take tentative steps, implement small policy changes, observe the effects of their actions, and learn from them as lessons accumulate, interact and slowly reinforce each other. Finally, learning does not always guarantee moderation and the adoption of democratic attitudes, tactics, and policies. As the article illustrates, the political learning process is often best characterized as highly contingent and complex.
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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 | 0.010 |
| 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 | 0.016 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".