On the development of criteria for determining the effectiveness of technical working groups: A case study about information processes in marine conservation and fisheries management in Belize
Bibliographic record
Abstract
Use of scientific information in evidence-based decision-making is critically important in addressing coastal and ocean management concerns. In an ecosystem-based management context, ensuring that the “right” information (reliable) is available can be particularly challenging as the information often resides in different organizations with different management mandates. Many governmental and intergovernmental organizations have used a range of approaches, including technical advisory committees and working groups, to facilitate multidisciplinary input into the development of appropriate policies and management practices. This study examined the roles of multiple stakeholders participating in technical working groups that assist in decision-making for marine fisheries management in Belize, a coastal country in Central America. Through interviews with members of three working groups – the Spawning Aggregation Working Group, the Managed Access Working Group, and the National Hicatee Conservation and Monitoring Network – and decision-makers in the Belize Fisheries Department, information production processes and pathways for information uptake into policy were investigated. Major characteristics of communication at the science-policy interface associated with each working group were revealed. Important communication enablers and barriers were identified related to the operation of the working groups, such as membership commitment and resource availability, which influenced knowledge exchange within and outside the groups. Based on the results, a set of requirements for the creation and operation of effective working groups was formulated with regard to requisite inputs, the operational processes, and the types and uses of the information outputs of the groups. These requirements serve as a foundation for development of indicators of the effectiveness of working groups in environmental management contexts that support communication and decision-making at science-policy interfaces. • Multidisciplinary working groups produce useful information for marine resource management in Belize • Working group attributes enable advice transfer to support fisheries management • Member commitment, resources, and group processes affect the success of working groups • Study of information production and use reveals requirements for effective working groups • Group effectiveness can be assessed by information input, output, and process requirements
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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 | 0.068 | 0.154 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.005 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".