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Record W7134855713 · doi:10.5281/zenodo.18937470

Initial report on policy obstacles and opportunities for the integration and uptake of citizen generated data and citizen & community-led actions in environmental compliance assurance - more4nature D1.1

2024· article· en· W7134855713 on OpenAlexaboutno aff
Eléonore Maitre-Ekern, Rachel Karasik, Caroline Enge, Line Johanne Barkved

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Conservation and Management
Canadian institutionsnot available
FundersEuropean CommissionUK Research and Innovation
KeywordsContext (archaeology)Status quoCompliance (psychology)Scope (computer science)Thematic analysisBest practiceSustainabilityProcess (computing)Intervention (counseling)

Abstract

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This report, as a contribution to the overall aim of the more4nature project, provides a sound understanding – based on a representative sample of international and regional policies – of the status quo regarding the role of citizen & community-led actions (CCLA) and citizen generated data (CGD) in environmental policies with regards to environmental compliance assurance (ECA). The concept of ‘citizen & community-led actions’ is being developed as part of the more4nature project and refers to actions that citizens and communities can take to improve environmental protection. CGD is often the product of actions by citizens and can notably be used to complement institutional data. The policy analysis in this report takes place within the context of the three thematic policy areas of the more4nature project, namely zero pollution, biodiversity protection and deforestation prevention (Z/B/D). This report analyses the three types of intervention of ECA in light of possible use of citizen science, and more specifically: Whether CCLA can be used in compliance promotion, i.e. to contribute to preventing and limiting non-compliance with a policy; Whether CGD can be used in compliance monitoring, i.e. to track and identify potential problems with compliance; and Whether CCLA and/or CGD can be used in compliance enforcement, i.e. to address, via legal or official channels, a situation of non-compliance. Twenty policies were selected for analysis in this report. Each of them was reviewed using selected keywords and a template resulting from a multidisciplinary collaboration with project partners. Every step and stages of the iterative methodological approach developed, implemented and refined to conduct the policy analysis is presented in detail: Step 1: Preparations, including the selection of policies, the selection of criteria and keywords, and the development of the template; Step 2: the policy analysis, i.e. the initial data collection, the review process and the collection of completed data; Step 3: the data analysis, including results extraction, identification of trends and preliminary findings, and drafting of the report. The preliminary findings show that ECA is largely a matter of national competence, whether a policy is adopted at EU or international level. Therefore, most policies analysed did not include many provisions or other specifications about how to conduct ECA. Generally, compliance enforcement was the type of intervention that was least present from policy texts. We found that in the policies we analysed, the terms used to refer to some forms of citizen involvement were mostly not those that define citizens science in the literature. Instead, the most relevant terms that we came across included ‘public’, ‘civil society’, ‘third parties’, ‘stakeholders’, ‘consultation’ and ‘participation’. With regards to the use of CCLA in compliance promotion, the most common occurrences referred to the provision of information to citizens, although some also included requirement of consultation or active public participation. The use of CGD in compliance monitoring was rarely directly specified in the policies, but some provisions contained elements that indicated either that such use could be possible or, on the contrary, that it could be restricted. In the latter case, the existence or extent of such restriction would most often depend on the interpretation of the international or EU provisions at the national level. The use of CCLA and CGD in compliance enforcement ranged among the policies, with some having minimal enforcement provisions and others allowing access to the public in judicial procedures (as applicants or as witnesses). Most opportunities for the use of CCLA and CGD in ECA were found in one horizontal policy - the Aarhus Convention on access to information, public participation in decision-making and access to justice in environmental matters, as well as in the most recently adopted Z/B/D policies that we assessed, namely Regulation (EU) 2023/1115 on Deforestation-free Products and the 2022 Kunming-Montreal Global Biodiversity Framework, the Environmental Crime Directive and the Nature Restoration Regulation (also known as ‘Nature Restoration Law’). These policies may show the way in terms of how future policies or revisions of existing ones may better promote the use of citizen science in ECA. However, it should be noted that our findings are based on an analysis of the text of selected policies and may not necessarily translate in practice.This report demonstrates that, on the one hand, there are opportunities for the use of CCLA and CGD in ECA within the EU and international context. Those are quite varied and stem both from horizontal and vertical policies. On the other hand, the report also exposes the difficulty of identifying such opportunities in environmental policies because of the vagueness of the language or the referral to the national (or local) level to decide whether to allow or restrict the use of CCLA and CGD. This may give room for agency through interpretation, but effectively leaves citizen science groups in the dark about how policies enable or even promote their participation in the ECA process.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.875
Threshold uncertainty score0.657

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.169
GPT teacher head0.318
Teacher spread0.149 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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