Benchmarking data of the Fair Trade USA Capture Fisheries Standard and the Marine Stewardship Council Fisheries Standard against the Food and Agricultural Organization's Voluntary Guidelines for Securing Sustainable Small-Scale Fisheries
Bibliographic record
Abstract
This paper presents data associated with the benchmarking of the Fair Trade USA (FT USA) Capture Fisheries Standard and the Marine Stewardship Council (MSC) Fisheries Standard against the Food and Agriculture Organization's Voluntary Guidelines for Securing Sustainable Small-Scale Fisheries in the Context of Food Security and Poverty Eradiation (FAO Voluntary Guidelines). Benchmarking was used to determine the extent to which these standards, which promote sustainability in different ways, align with the FAO Voluntary Guidelines. The data represent a comprehensive analysis of these standards and are useful for beginning to understand the appropriateness of these standards for small-scale fisheries in developing regions of the world. For further interpretation and discussion please see “A tale of two standards: A case study of the Fair Trade USA certified Maluku handline yellowfin tuna ( Thunnus albacares ) fishery” [1] .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.017 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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 source (direct Gemma or distilled Codex), 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".