Weak states and the commons: Fisheries and economic development in the Gaspé Peninsula circa 1830
Bibliographic record
Abstract
The inefficiencies of common property fisheries are well-known to economists. To avoid over-exploitation, they propose multiple forms of government solution such as taxes, quotas and the enforcement of property rights regimes designed to avoid over-harvesting. But can efficient arrangements also exist under statelessness, or in the presence of weak states? One such example is the Gaspé Peninsula (in the Canadian province of Quebec) during the first half of the nineteenth century. There, a single firm (the Charles Robin Company) came to dominate the market and was able to restrict entry effectively. In this paper, we explain that it was able to do so by reducing the prices on imported goods that it would give to local fishermen in exchange for a part of their catch. This had the effect of deterring fishermen from contracting with other merchants as well as deterring other merchants from entering the market. It also made the region richer than most regions of Canada at the time, contrary to what historians have depicted. We take this as an example of the ability to deal with commons problems in the presence of weak states.
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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 | 0.000 |
| 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".