Improvements to Rapfish: a rapid evaluation technique for fisheries integrating ecological and human dimensions<sup>a</sup>
Bibliographic record
Abstract
This paper reports recent developments in Rapfish, a normative, scalable and flexible rapid appraisal technique that integrates both ecological and human dimensions to evaluate the status of fisheries in reference to a norm or goal. Appraisal status targets may be sustainability, compliance with a standard (such as the UN code of conduct for responsible fisheries) or the degree of progress in meeting some other goal or target. The method combines semi-quantitative (e.g. ecological) and qualitative (e.g. social) data via multiple evaluation fields, each of which is assessed through scores assigned to six to 12 attributes or indicators: the scoring method allows user flexibility to adopt a wide range of utility relationships. For assessing sustainability, six evaluation fields have been developed: ecological, technological, economic, social, ethical and institutional. Each field can be assessed directly with a set of scored attributes, or several of the fields can be dealt with in greater detail using nested subfields that themselves comprise multidimensional Rapfish assessments (e.g. the hierarchical institutional field encompasses both governance and management, including a detailed analysis of legality). The user has the choice of including all or only some of the available sustainability fields. For the attributes themselves, there will rarely be quantitative data, but scoring allows these items to be estimated. Indeed, within a normative framework, one important advantage with Rapfish is transparency of the rigour, quality and replicability of the scores. The Rapfish technique employs a constrained multidimensional ordination that is scaled to situate data points within evaluation space. Within each evaluation field, results may be presented as a two-dimensional plot or in a one-dimensional rank order. Uncertainty is expressed through the probability distribution of Monte-Carlo simulations that use the C.L. on each original observation. Overall results of the multidisciplinary analysis may be shown using kite diagrams that compare different locations, time periods (including future projections) and management scenarios, which make policy trade-offs explicit. These enhancements are now available in the R programming language and on an open website, where users can run Rapfish analyses by downloading the software or uploading their data to a user interface.
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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 | 0.021 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.031 | 0.016 |
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".