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Enregistrement W4297075051 · doi:10.5281/zenodo.7112842

Deliverable 4.1 Community based monitoring programmes in the Arctic: Capabilities, good practice and challenges

2018· report· en· W4297075051 sur OpenAlexaboutno aff
Finn Danielsen, Maryann Fidel, Noor Johnson, Michael K. Poulsen, Hajo Eicken, Ania Alba, Simone G. Hansen, Lisbeth Iversen, M. B. Enghoff, Olivia Lee, Peter Pulsifer, Peter Thorne

Notice bibliographique

RevueZenodo (CERN European Organization for Nuclear Research) · 2018
Typereport
Langueen
DomaineSocial Sciences
ThématiqueDelphi Technique in Research
Établissements canadiensnon disponible
Organismes subventionnairesBureau of Ocean Energy ManagementInternational Arctic Science Committee
Mots-clésDeliverableThe arcticArcticGood practiceEnvironmental planningCommunity practiceEnvironmental resource managementEnvironmental scienceComputer scienceSystems engineeringEngineeringOceanographyMedicineEngineering ethicsGeologyNursing

Résumé

récupéré en direct d'OpenAlex

Arctic people live in and observe the Arctic environment year-round. Intimate knowledge of the environment and environmental changes is fundamental to the survival of these people who frequently depend on natural resources for their livelihood. This report presents a review of the capabilities, good practices, opportunities and barriers of community-based environment monitoring programmes in the Arctic, with a focus on decision-making for resource management. <br> The review builds upon previous work that contributed to the Sustaining Arctic Observing Networks (SAON, Task #9; Johnson et al. 2016). We first identified 170 community-based monitoring programmes in the Arctic from the peer-reviewed literature and from searching the internet. Then we chose 30 programmes that reflect the widest possible set of situations and issues for more in-depth analysis. We reviewed the scientific literature and discussed experiences at workshops with practitioners and community members engaged in monitoring programmes in Nuuk in 2016 and in Fairbanks, Quebec and the Russian communities of Komi and Zhigansk in 2017. Key findings are summarized below. <strong>Capabilities</strong>. There is a long history of involving community members of all ages in monitoring the Arctic. Programmes involve various organizations, including community groups, all levels of government, universities, schools and the private sector. Programmes monitor biological attributes, abiotic phenomena and socio-cultural attributes, often within the same framework. The observing domains that receive the greatest attention are ”land/cryosphere” and ”ocean/sea-ice”. By their nature, community-based monitoring programmes tend to focus on those issues of greatest concern to local stakeholders, thus having considerable potential to influence on-the-ground management activities. <br> The programmes complement scientist-executed monitoring by utilizing different methodologies, engaging the experience of Indigenous knowledge holders and other long-term residents who have significant knowledge of the environment, and by enabling an increase in sample size or density, area and time. Most Arctic community-based monitoring programmes make observations between 61-70°N. They cover all the Arctic biomes with the exception of ice desert. Data are typically collected throughout the year. The majority of the programmes involve Indigenous knowledge. The programmes inform decisions at local, regional and national levels and often provide insight into processes and changes not captured in agency or research-driven monitoring programmes. Thus, they contribute to better informed decisions or better documented processes within the key economic sectors in the Arctic: - Fisheries,<br> - Forestry,<br> - Herding,<br> - Hunting,<br> - Mineral and hydrocarbon extraction,<br> - Shipping, and<br> - Tourism. <br> Methods originating from both the natural and social sciences are often used, increasingly also drawing on Indigenous knowledge for programme design. New technologies enable the programmes to collect data and communicate findings with greater certainty than ever before. Some programmes have made their data publicly available, but few have links to data discovery portals or global repositories. The programmes have the potential to contribute to monitoring progress in relation to ten international environmental agreements of particular relevance to the Arctic. They could also contribute to achieving 16 of the 17 United Nations Sustainable Development Goals. <strong>Good practices. </strong>Arctic community-based monitoring programmes are diverse, with many successful approaches. By providing actionable information to management authorities and community members, the programmes inform many kinds of decisions. Web based knowledge management platforms are increasingly used for data storage and communication. Credible knowledge products are obtained in many ways, including through careful planning, thorough guidance of the participants, and validation of data through different approaches. Many programmes follow the principles of “Free, Prior and Informed Consent” and contribute, directly or indirectly, to protecting the rights of the Indigenous and local communities. Co-design of programmes and co-production of knowledge can help ensure relevance and utility of monitoring data. <br> <strong>Opportunities and barriers. </strong>Community-based monitoring offers strong potential for linking environmental monitoring to awareness raising and enhanced decision-making at all levels of resource management. Moreover, community-based monitoring programmes could provide hypotheses and data that potentially could fill gaps in climate modelling and in research within such areas as risk management, safety, and food and water security. Community-based monitoring is also a way to fulfill the rights of the citizens to take part in decisions that are related to their local and regional areas and to be able to take part in the knowledge production in order to develop and safeguard their environment. <br> One barrier to maximizing the potential of community-based monitoring for decision-making has been the perception that information from local people is subjective and anecdotal. Today, a growing body of literature demonstrates that where Indigenous and local knowledge has been systematically gathered, the data collected by community members are comparable to those arising from professional scientists. Another barrier is that management authorities are sometimes slow at operationalizing or acting upon community observations in their decision-making. Regardless of this, involving people who face the daily consequences of environmental challenges in monitoring can help in adapting decision-making on natural resource management to local realities in a rapidly changing Arctic.

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)0,024
score de la tête « metaresearch » (Gemma)0,019
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche, Études des sciences et des technologies, Communication savante, Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Autre · Signal consensuel: aucune
Score de désaccord entre enseignants0,969
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0240,019
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0060,001
Communication savante0,0020,000
Science ouverte0,0020,001
Intégrité de la recherche0,0000,002
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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 Opus0,275
Tête enseignante GPT0,410
Écart entre enseignants0,135 · 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.

Devis d'étudeSans objet
Domainenon disponible
GenreAutre

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
Publié2018
Routes d'admission1
Résumé présentoui

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