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Enregistrement W2985762854 · doi:10.1186/s13750-019-0181-3

Bridging Indigenous and science-based knowledge in coastal and marine research, monitoring, and management in Canada

2019· article· en· W2985762854 sur OpenAlex

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Notice bibliographique

RevueEnvironmental Evidence · 2019
Typearticle
Langueen
DomaineHealth Professions
ThématiqueIndigenous Studies and Ecology
Établissements canadiensCarleton UniversityEnvironment and Climate Change CanadaUniversity of WaterlooFisheries and Oceans Canada
Organismes subventionnairesFisheries and Oceans CanadaNatural Sciences and Engineering Research Council of Canada
Mots-clésGrey literatureTraditional knowledgeIndigenousKnowledge baseEnvironmental resource managementGeographyBridge (graph theory)Knowledge managementLibrary scienceEcologyMEDLINEComputer sciencePolitical scienceBiologyEnvironmental science

Résumé

récupéré en direct d'OpenAlex

Abstract Background Drawing upon multiple types of knowledge (e.g., Indigenous knowledge, local knowledge, science-based knowledge) strengthens the evidence-base for policy advice, decision making, and environmental management. While the benefits of incorporating multiple types of knowledge in environmental research and management are many, doing so has remained a challenge. This systematic map examined the extent, range, and nature of the published literature (i.e., commercially published and grey) that seeks to respectively bridge Indigenous and science-based knowledge in coastal and marine research and management in Canada. Methods This systematic map applied standardized search terms across four databases focused on commercially published literature, carefully selected specialist websites, and two web-based search engines. In addition, reference sections of relevant review articles were cross-checked to identify articles that may not have been found using the search strategy. Search results were screened in two sequential stages; (1) at title and abstract; and (2) at full text following a published protocol. All case studies included were coded using a standard questionnaire. A narrative synthesis approach was used to identify trends in the evidence, knowledge gaps, and knowledge clusters. Results A total of 62 articles that spanned 71 Canadian case studies were included in the systematic map. Studies across the coastal and marine regions of Inuit Nunangat accounted for the majority of the studies. Whether the focus is on management and decision making or research and monitoring, the predominant ecological scale was at the species level, accounting for over two-thirds of the included studies. There were 24 distinct coastal and marine species of central focus across the studies. Nunavut had the greatest taxonomic coverage as studies conducted to date cover 13 different genera. The predominant methodology employed for combining and/or including Indigenous knowledge was case study design, which accounted for over half of the studies. Other methodologies employed for combining and/or including different ways of knowing included: (i) community-based participatory research; (ii) mixed methods; (iii) ethnography; and (iv) simulation modelling. There are a suite of methods utilized for documenting and translating Indigenous knowledge and an equally diverse tool box of methods used in the collection of scientific data. Over half of the case studies involved Indigenous knowledge systems of the Inuit, while another significant proportion involved Indigenous knowledge systems of First Nations, reflecting 21 unique nations. We found that demographics of knowledge holders were generally not reported in the articles reviewed. Conclusions The results of this systematic map provide key insights to inform and improve future research. First, a variety of methodologies and methods are used in these types of studies. Therefore, there is a need to consider in more detail how Indigenous and science-based knowledge systems can be respectively bridged across subjects while also recognizing specific place-based needs of Indigenous communities. Second, the work highlights the need to better report the demographics of knowledge holders. Further inquiry focused on the extent of knowledge co-production and assessing Indigenous participation across different stages of the research process would serve the research community well to improve future research and monitoring in support of, and to strengthen, evidence-based environmental management.

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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: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,179
Score d'incertitude au seuil0,839

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 technologies0,0010,000
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 Opus0,053
Tête enseignante GPT0,374
Écart entre enseignants0,320 · 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