Cultivating Trust: Norms, Institutions and the Implications of Scale
Bibliographic record
Abstract
We study the co-evolution of norms and institutions in order to better understand the conditions under which potential gains from new trading opportunities are realized. New trading opportunities are particularly vulnerable to opportunistic behavior and therefore tend to provide fertile ground for cheating. Cheating discourages production, raising equilibrium prices and therefore the return to cheating, thereby encouraging further cheating. However, such conditions also provide institutional designers with relatively high incentives to improve institutions. We show how an escape from the shadow of opportunism requires that institutional improvements out-pace the deterioration of norms. A key prediction from the model emerges: larger economies are more likely to evolve to steady states with strong honesty norms. This prediction is tested using a cross section of countries; population size is found to have a significant positive relationship with a measure of trust, even when controlling for standard determinants of trust and institutional quality.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".