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Enregistrement W7134804428 · doi:10.5281/zenodo.18937471

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· W7134804428 sur OpenAlexaboutno aff
Eléonore Maitre-Ekern, Rachel Karasik, Caroline Enge, Line Johanne Barkved

Notice bibliographique

RevueOpen MIND · 2024
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueEnvironmental Conservation and Management
Établissements canadiensnon disponible
Organismes subventionnairesEuropean CommissionUK Research and Innovation
Mots-clésContext (archaeology)Status quoCompliance (psychology)Scope (computer science)Thematic analysisBest practiceSustainabilityProcess (computing)Intervention (counseling)

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Autre devis · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,962
Score d'incertitude au seuil0,391

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,237
Tête enseignante GPT0,375
Écart entre enseignants0,138 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeAutre devis
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

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