From in-person to virtual engagement: Adaptations of a participative process for designing a marine litter public policy in Brazi
Bibliographic record
Abstract
Marine litter is a transversal issue that affects the envi- ronment and society in a multitude of ways. As such, solutions to this problem are complex and demand the engagement of multiple sectors of society. The São Paulo Strategic Plan for Monitoring and Assessment of Marine Litter (PEMALM) is the first public policy of its kind, seeking to establish indicators and build knowledge to move towards a plan to combat marine litter in the most populous state in Brazil. From its inception, PEMALM has sought to establish a participative construction frame- work, involving key stakeholders at each step. When the Covid-19 pandemic struck, the participative construction process had to be adapted. Here we present and discuss the strategies applied in the participatory process of PEMALM to guarantee the remote engagement of stakeholders. Three participatory milestones were part of the final policy-making process: a first in-person workshop which gathered stakeholders in a single location, a series of in-person meetings in which the project team travelled to where the stake- holders are located, and, due to the Covid-19 pandemic, an entirely virtual workshop. Sector participation was found to be alike for online and on-site events, with higher participation of the public sector, followed by academia, NGOs and the private sector in both. The adjustments and the adaptive effort placed on the participatory process due to the Covid-19 pandemic, such as being dedicated and attentive to the needs of attendees, expanding the modes of interaction and promoting a flexible and light schedule to reduce online fatigue, guaranteed the quality of stakeholder engagement and participation. The positive accomplishments of the hybrid strategy used in building PEMALM as a public policy exemplifies ways to facilitate and broaden participation in the co-construction under mobility restrictions.
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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 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.017 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".