Public Participation in EU Legislation? Recommendations for Involving Citizen Scientists in Anthropogenic Litter Research Within the Water Framework Directive
Bibliographic record
Abstract
ABSTRACT Anthropogenic litter causes significant harm to the environment on a global scale. Achieving international agreements and establishing corresponding national legislation is essential for solving this prevalent environmental problem. Effective monitoring programmes are also critical for evaluating the environmental status in aquatic (marine and freshwater) environments, as required by the Water Framework Directive (WFD) and the Marine Strategy Framework Directive (MSFD) in Europe. In contrast to the MSFD, the current version of the WFD does not yet include anthropogenic litter pollution as an indicator to evaluate the status of aquatic environments. In order to overcome these shortcomings, we recommend using existing litter data generated by citizen science initiatives as a baseline to establish relevant indicators in the WFD. Further, citizen scientists could contribute to the WFD by taking complimentary samples, for example, at underrepresented smaller streams, adding context and value to data collected at established monitoring stations. The involvement of citizens as actors within an EU Directive would not only help to obtain valuable data on a significant spatial and temporal scale but could potentially also increase the environmental awareness and political engagement of the public. The upcoming revision cycle of the WFD in 2028 presents a unique opportunity to give citizens a voice and opportunity to partake in EU legislative frameworks.
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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 | 0.305 | 0.278 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.027 | 0.029 |
| Open science | 0.011 | 0.025 |
| Research integrity | 0.079 | 0.033 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".