Citizen science data, marine plastics, and SDG monitoring: How to build trust in citizen science data and methodologies among diverse actors with varying needs and motivations?
Bibliographic record
Abstract
The accumulation of plastic litter in marine environments presents a major environmental challenge to sustainability and is central to the United Nations (UN) Sustainable Development Goals (SDGs). However, the vast size of oceans and the widespread nature of marine plastic litter make its monitoring difficult. Citizen science offers a promising solution, providing valuable data for SDG monitoring and reporting, however, there has been no evidence of its use to date. In this presentation, we share how Ghana became the first country to integrate citizen science data into their official statistics and the official monitoring and reporting of SDG indicator 14.1.1b for marine plastic litter. This effort also helped to bridge local, community level data collection with national and global monitoring and policy agendas, aligning with the SDG framework. The data have already contributed to Ghana's Voluntary National Review and been reported in the UN SDG Global Database, helping to inform national policies.In this presentation, we will focus on the process of validating citizen science data and integrating it into official monitoring and reporting, involving key stakeholders at local, national, and global levels, such as government agencies, the UN, civil society organizations, citizen science networks, and academia. This approach offers a model for other countries and citizen science initiatives interested in adopting similar methods for official monitoring and policymaking. A central theme will be how citizen science projects can be designed to foster collaboration and trust among diverse stakeholders, including governments, UN bodies, and local communities. We will highlight our success and lessons learnt, and showcase how knowledge production through citizen science can strengthen sustainability efforts, influence effective policy, and highlight the value of participatory sciences.
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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 | 0.323 | 0.488 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.014 | 0.042 |
| Scholarly communication | 0.038 | 0.068 |
| Open science | 0.006 | 0.035 |
| Research integrity | 0.016 | 0.022 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".