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Enregistrement W2091734178 · doi:10.1657/1938-4246-42.4.379

Commentary. Integrating Research, Education, and Traditional Knowledge in Ecology: a Case Study of Biocomplexity in Arctic Ecosystems

2010· article· en· W2091734178 sur OpenAlexaboutno aff
William A. Gould, Grizelle González, Donald A. Walker, Chien‐Lu Ping

Notice bibliographique

RevueArctic Antarctic and Alpine Research · 2010
Typearticle
Langueen
DomaineEarth and Planetary Sciences
ThématiqueClimate change and permafrost
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésOutreachValue (mathematics)Environmental educationMainstreamScience educationPopulationEducational researchSociologyPolitical scienceEngineering ethicsPublic relationsEcologyPedagogyEngineeringBiology

Résumé

récupéré en direct d'OpenAlex

Integrating research and education is a fundamental goal of institutions and agencies supporting science because of the benefits to society of a more informed and scientifically literate population. The value of engaging public interest in ecological research is to maintain support for and integrate science in solutions to environmental problems (Hudson, 2001; Avila, 2003). The National Science Foundation lists criteria for assessing broader impacts of research projects which include (1) the integration of research and education—advancing discovery and understanding while promoting teaching, training, and learning; and (2) developing opportunities to broaden the participation of groups underrepresented in science (NSF, 2006). Educational researchers are developing models and assessing outcomes of integrating research and education at diverse grade levels (Trautmann and Krasny, 2006; Bowen and Roth, 2007); the value of integrating traditional ecological knowledge (TEK) in research has been demonstrated in several ecosystems (Huntington, 2000; Kimmerer, 2002; Ford et al., 2007; Garcia-Quijano, 2007) but specific approaches and achievements of efforts integrating research and education are not widely disseminated in environmental research journals. Thus, while there is a call for environmental scientists to broaden their activities to engage in outreach (i.e., have broader impact) there is a lag in the assessment of the effectiveness of these activities and of their value in mainstream scientific culture. Environmental scientists seldom evaluate these impacts, and there are few venues or incentives to report on these activities in ways that would enhance their research careers. For an individual scientist, efforts expended in integrating research and education often occur at the expense of research productivity and this results in a lack of reward for a researcher’s efforts to broaden research impacts (Andrews et al., 2005; Uriarte et al., 2007). One way to address the imbalance between efforts devoted to broader impacts vs. avenues for reporting on these efforts is through the publication of case studies and assessments of integration efforts in journals that reach a research audience as opposed to an education audience. This venue exists in a very few, high-profile, broad-interest research journals (e.g., Science, Bioscience) but could be more widespread in journals addressing a range of environmental research. Examples of successful integration help researchers and institutions evolve better mechanisms to achieve goals beneficial to society, including improved public understanding of science, greater diversity of research and stakeholders, and better application of current scientifically based information to managing environmental issues. In that spirit, we present as an example an effort integrating an interdisciplinary research project investigating the interactions of climate, vegetation, and permafrost in the study Biocomplexity of Arctic Tundra Ecosystems with a university field course, Arctic Field Ecology, and with indigenous Inuit students and elders. The integration allowed university students and native community members to participate with the research team, drawn by the opportunity to gain education and experience. This participation has had synergistic benefits with the research agenda and diversified the pool of stakeholders involved in the research (see Box 1).

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)0,005
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
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 enseignants0,433
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0050,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,001
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
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 Opus0,188
Tête enseignante GPT0,401
Écart entre enseignants0,213 · 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'étudeObservationnel
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

Citations5
Publié2010
Routes d'admission1
Résumé présentoui

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