Spies and swords: Behavior in environments with costly monitoring and sanctioning
Bibliographic record
Abstract
Depletion of resources is a serious global concern. Fisheries on both Canadian coasts have, at various times, been closed and thousands of people put out of work. Excessive numbers of whale watchers have led to a reduction in the number of whales returning to – and consequently declining biodiversity of – the Bay of Fundy (Hawkins, 1998: B1). Groundwater in the American Midwest is used faster than it is replenished. Fuelwood lots and grazing lands suffer from severe depletion around the world. To understand that these issues are a problem, one need only look at the socio-economic impacts upon the relevant population of users. To understand how these issues became a problem, however, the interaction between individuals, groups, and the resource must be examined. In this chapter, the specific effects of costly monitoring of individuals and the role of endogenous sanctions are examined.
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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 | 0.000 |
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".