A framework for selecting a suite of indicators for fisheries management
Bibliographic record
Abstract
Abstract We develop a framework for the objective selection of a suite of indicators for use in fisheries management. The framework encompasses eight steps, and provides guidance on pitfalls to be avoided at each step. Step 1 identifies user groups and their needs, featuring the setting of operational objectives, and Step 2 identifies a corresponding list of candidate indicators. Step 3 assigns weights to nine screening criteria for the candidate indicators: concreteness, theoretical basis, public awareness, cost, measurement, historic data, sensitivity, responsiveness, and specificity. Step 4 scores the indicators against the criteria, and Step 5 summarizes the results. Steps 3–5 offer technical aspects on which guidance is provided, including scoring standards for criteria and a generalized method for applying the standards when scoring individual indicators. Multi-criterion summarization methods are recommended for most applications. Steps 6 and 7 are concerned with deciding how many indicators are needed, and making the final selection of complementary suites of indicators. Ordinarily, these steps are done interactively with the users of the indicators, thus providing guidance on process rather than technical approach. Step 8 is the clear presentation to all users of the information contained. The discussion also includes the special case in which indicators are used in formal decision rules.
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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 | 0.080 | 0.074 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.025 | 0.015 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".