Keys for effective multistakeholder science committees: The importance of structure, processes and desired behaviours
Bibliographic record
Abstract
Abstract Multistakeholder Science Committees (MSC's) are increasingly used to inform science assessments and syntheses supporting the development of environmental regulations and policies. By bringing together a diversity of pertinent rights holders and stakeholders they can create holistic and inclusive outcomes that would not otherwise be produced if participants are drawn from a narrow range of expertise and jurisdictional representation. However, MSC's also create notable challenges. As practitioners and leaders of a multitude of MSC's, we draw from our collective experiences to identify key steps we feel will ensure high performance of MSC's. We highlight the importance of recognizing the mandate letter and developing a jointly agreed‐upon Terms of Reference (TOR) and then identifying specific aspects related to: (i) roles and responsibilities of participants; (ii) founding principles required to enable the work of a MSC; (iii) acceptable behaviours for MSC members; and (iv) mechanisms to assess the consequences when undesired behaviours and actions are displayed. We also identify actions and behaviours that will most likely result in the failure of the MSC. Practical implication: The ability to diagnose the underlying causes of challenges to MSC's performance, provides opportunities to resolve them so that the performance can be improved and undesirable actions and behaviours are minimized or avoided.
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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 | 0.162 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.013 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".