Integrating citizen science data into European legislation: a critical examination of the pathway from data to policy.
Bibliographic record
Abstract
The current environmental urgency means that we need data on the ocean, to understand its changes and potential solutions. Although citizen science data have been identified as a potential contribution to meeting international requirements (Fraisl et al., 2020; Fraisl et al., 2023; Danielsen et al., 2024), there is still a long way to go. The reporting requirements of official monitoring programmes, e.g. under MSFD 2008/56 EC or WFD 2000/60 EC, are still reluctant to integrate citizen science data, while some successful experiences at national level are already in place (e.g. descriptor 10 MSFD). On the other hand, due to the establishment of data libraries for the digital twin of the ocean there is an urgent requirement for data, including citizen science. Furthermore, consolidated structures are gathering and validating citizen science data, such as specialised structures such as the MINKA citizen science observatory, or international aggregators such as EMODnet and GBIF. However, there are still some gaps in the data pipeline from data to policy mainly for the data citation and data providers acknowledgement. Despite significant data collection efforts and standardization initiatives, achieving consistent data quality remains challenging in citizen science due to diverse stakeholder requirements and varying accuracy levels across projects (Balázs et al, 2021; European Commission, 2021). However, several European-funded projects are currently working on data validation, accreditation and data provider recognition such as MINKE Project (Metrology for Integrated Marine Management and Knowledge-Transfer Network), ENFORCE (Empowering Citizen for Environmental Action), Guarden (safeGUARDing biodiversity and critical ecosystem services across sectors and scales) and Marine biodiversity monitoring harmonisation. Nevertheless, in order to achieve official recognition of citizen science, it is essential that international, European institutions and Member States facilitate its integration into monitoring programmes and environmental agreements within the regulatory framework. The research infrastructures could have the potential to facilitate data accreditation for end-users in terms of the data management processes. A showcase of best practices on how citizen science can shape environmental compliance supporting legislative requirements will be provided, focusing on both realised benefits and untapped potential. ReferenceB Balázs, P Mooney, E Nováková, et al, 2021, Data Quality in Citizen Science Chapter 8 in K. Vohland et al. (eds.), The Science of Citizen Science, https://doi.org/10.1007/978-3-030-58278-4_8Danielsen, F, Ali, N, Andrianandrasan, H.T., et al, 2024. Involving citizens in monitoring the Kunming-Montreal Global Biodiversity Framework. Nat Sustain (2024). https://doi.org/10.1038/s41893-024-01447-yEuropean Commission: Joint Research Centre, Mitton, I., Tricarico, E., Schade, S., Lopez Canizares, C. et al., Data-validation solutions for citizen science data on invasive alien species, Publications Office of the European Union, 2021, https://data.europa.eu/doi/10.2760/694386Fraisl, D, Campbell, J, See, L, et al., 2020. Mapping citizen science contributions to the UN sustainable development goals. Sustainability Science, 15(6): 1735–1751. DOI: https://doi.org/10.1007/s11625-020-00833-7Fraisl,D, See, L, Campbell, J, et al, 2023, The Contributions of Citizen Science to the United Nations Sustainable Development Goals and Other International Agreements and Frameworks Citizen Science: Theory and Practice 8 (1): 27. DOI: 10.5334/cstp.643
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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 | 0.317 | 0.346 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.016 | 0.027 |
| Science and technology studies | 0.012 | 0.037 |
| Scholarly communication | 0.053 | 0.066 |
| Open science | 0.009 | 0.026 |
| Research integrity | 0.029 | 0.026 |
| Insufficient payload (model declined to judge) | 0.006 | 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".