Making Cents Out of Barter Data from the British Columbia Groundfish ITQ Market
Why this work is in the frame
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Bibliographic record
Abstract
Quota prices in fisheries managed with individual transferable quotas (ITQs) can reveal information about the fishery that can be useful to participants and fishery managers. However, there has been relatively little study of quota markets and no published analysis of market transactions that involve barter (quota for quota) transactions which are frequently observed in multispecies ITQ systems. I propose a modified hedonic method for estimating implicit prices from these transactions and test it with Monte Carlo experiments before applying it to quota market data. The empirical analysis of implicit quota pound values from barter trades in the British Columbia groundfish ITQ supports anecdotal evidence that quota pound values do not rise above ex-vessel values even for “bycatch” species that are constraining catch of other species.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.150 | 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 it