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Enregistrement W6889480856 · doi:10.25607/obp-1840

INTAROS Community-Based Monitoring Experience Exchange Workshop Report, Québec City, Québec , December 11 to 12, 2017.

2018· report· en· W6889480856 sur OpenAlexaboutno aff

Notice bibliographique

RevueIOC of UNESCO (Intergovernmental Oceanographic Commission) · 2018
Typereport
Langueen
Domaine
Thématique
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésVariety (cybernetics)ConventionInformation exchangeCapacity buildingInformation sharingSummative assessmentData sharingBest practice

Résumé

récupéré en direct d'OpenAlex

This INTAROS Community-Based Monitoring Workshop was held in Québec at the Québec Convention Centre on December 11-12, 2017 concurrently with the Arctic Change 2017 Conference. The workshop offered an opportunity for practitioners of community-based monitoring (CBM) and observing programs from northern Canada to come together to exchange experiences and perspectives. Representatives of ten CBM programs attended; additional participants included representatives of co-management boards, northern research institutions, Inuit organizations, philanthropic organizations, and programs focused on developing or adapting tools for data management and sharing. The objective of the Quebec workshop was to facilitate exchange of ideas and information among CBM practitioners from Canada. An agenda for the workshop was developed based on input from participants. The agenda included time for brief presentations from CBM programs, breakout and plenary discussion groups, and time for networking over meals and games. The conclusions of the discussions at the workshop are summarized below. The motivations for implementing CBM programs differ but included: influencing decisions about industrial development and regulations in fishing and hunting; gaining a better understanding of the challenges and opportunities of climate change and social and human health conditions, as well as education and capacity building. Similarly, the motivation for individuals to be involved in CBM varied but included addressing the practical needs of communities. Other sources of motivation for individuals included developing a better understanding the environment, and sharing knowledge and learning from each other. There were a variety of attributes being monitored by the CBM programs in attendance, although there were still many information needs and gaps identified. A variety of people and organizations are using CBM generated information including: individuals, hunter trapper organizations, civil society organisations, industry, and government organizations at all levels, especially wildlife management agencies. Good practices are considered practices that have proven to work well for CBM programs. These included CBM practices that are supported by the community, provide capacity building opportunities, link Traditional Knowledge (TK) and science, and document TK. Trust among community members and scientists is also important. Challenges that CBM program representatives have faced included the ability to secure long term funding leading to gaps in data records over time. Other challenges included reconciling science and community priorities, linking quantitative with qualitative approaches, and meaningful dissemination of information. There were also challenges related to avoiding misconceptions of how the data can be used, timeliness of producing accessible data, community burnout, and difficulties of growing a program. Other challenges included a lack of technical support, limitations in community infrastructure and connectivity, and difficulties in influencing change. There was also a general agreement that CBM programs need to evolve, building on what we have learned rather than doing things the way they have always been done. In terms of sustainability of CBM, it was concluded that CBM sustainability can be enhanced through partnerships and working together. This could lead to shared data platforms and better coordinated efforts to reduce redundancy. CBM programs that are able to be relevant and address the needs of communities, scientists and decision makers are more likely to be sustained. With regards to contributing to decision making, it is important for CBM information to be included in decisions about industrial development. Decision makers often need to understand large scale processes. For CBM data to contribute, it needs to be interoperable (able to be analyzed across different programs). This is sometimes difficult since CBM programs and community priorities vary. With regards to data and data collection in CBM programs, methods of data collection must be culturally appropriate. Community consultation to create data sharing agreements should happen before a project is implemented. All parties need to be clear on what happens to data after it is collected. The community should have the opportunity to verify the data and decide what to make publicly available. CBM organizers need to take into account the connectivity and infrastructure of rural communities. Data and information needs to be returned to communities, not just in summary form, but also the raw data. A repository of data should be available to community members to meet current and future information needs. The technical challenges to data sharing are not as great as the jurisdictional and political challenges to data sharing. Successful CBM programs build on mutual respect and understanding, which comes from listening and educating oneself. Certain people are talented at building bridges between science and Arctic communities. CBM programs ought to hire and support these individuals. It is important to consider the implications of the CBM program on Indigenous rights. Participants recognized that working together will improve long term success of CBM. Benefits of a network could include many aspects. It could help researchers from outside the community understand where the gaps are in what is being monitored and avoid duplication of efforts. A network could contribute to better employment and training and capacity building opportunities (e.g. could potentially provide small grants to facilitate skill building and knowledge exchange of CBM programs). It could facilitate exchange of information to learn from the mistakes and successes of others, in addition to better understanding how other communities have successfully dealt with change. A network could advocate for CBM to be valued in decision making, risk management, and economic development, and for changes to funding structures. A CBM network would need to be flexible, as communities are diverse. It is important to provide benefits to network participants, and recognize that participation may vary over time. The report concludes with a number of suggested good practices and needs for CBM and observing programs in northern Canada.

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)0,004
score de la tête « metaresearch » (Gemma)0,002
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Études des sciences et des technologies, Intégrité de la recherche, Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesMéta-épidémiologie (sens strict)
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,471
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0040,002
Méta-épidémiologie (sens strict)0,0030,003
Méta-épidémiologie (sens large)0,0030,002
Bibliométrie0,0020,002
Études des sciences et des technologies0,0020,002
Communication savante0,0000,001
Science ouverte0,0050,005
Intégrité de la recherche0,0010,003
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

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 Opus0,087
Tête enseignante GPT0,347
Écart entre enseignants0,260 · 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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeSans objet
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
Publié2018
Routes d'admission1
Résumé présentoui

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