A comparative analysis of sustainable fishery development indicator systems in Australia and Canada
Bibliographic record
Abstract
Abstract This paper comparatively analyzes the systems in Australia and Canada from the perspective of the United Nations Food and Agriculture Organisation's Technical Guidelines for Responsible Fisheries No. 8 . The results show that the key factors in the success of the Australian system are public participation, selecting an indicator with its objectives and improving management performance by the evaluation of the system. Further, the boundaries of the SFDIS should be the same as the boundaries of the management units and fisheries should be examined independently. The framework chosen by the Canadian system is more all‐round, and can be combined with the PSR framework to maximize the management effects. Finally, techniques and specialist software such as fuzzy AHP etc. are ‘well‐suited to measuring weights and have the potential to be applied elsewhere’. Visual presentation is the best way to promote communication with the public. The United Nations Food and Agriculture Organisation's kite diagram and the Sustainable Development Committee's dashboard of sustainability are two excellent visualizations. Copyright © 2006 John Wiley & Sons, Ltd and ERP Environment.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.017 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".