Bibliographic record
Abstract
In How Informal Institutions Matter, Zeki Sarigil examines the role of informal institutions in sociopolitical life and addresses the following questions: Why and how do informal institutions emerge? To ask this differently, why do agents still create or resort to informal institutions despite the presence of formal institutional rules and regulations? How do informal institutions matter? What roles do they play in sociopolitical life? How can we classify informal institutions? What novel types of informal institutions can we identify and explain? How do informal institutions interact with formal institutions? How do they shape formal institutional rules, mechanisms, and outcomes? Finally, how do existing informal institutions change? What factors might trigger informal institutional change? In order to answer these questions, Sarigil examines several empirical cases of informal institution as derived from various issue areas in the Turkish sociopolitical context (i.e., civil law, conflict resolution, minority rights, and local governance) and from multiple levels (i.e., national and local).
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".