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Enregistrement W2588234694 · doi:10.2505/4/tst16_083_04_31

Many Ways of Knowing: A Multilogical Science Lesson on Climate Change

2016· article· en· W2588234694 sur OpenAlexaboutno aff
Martha M. Canipe, Sara Tolbert

Notice bibliographique

RevueThe Science Teacher · 2016
Typearticle
Langueen
DomaineHealth Professions
ThématiqueIndigenous Studies and Ecology
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésIndigenousTraditional knowledgeScience educationNatural (archaeology)Climate scienceNinthEnvironmental ethicsClimate changeSociologyGeographyEcologyPedagogyArchaeology

Résumé

récupéré en direct d'OpenAlex

[ILLUSTRATION OMITTED] Native science is ... a map of natural reality drawn from the experience of thousands of human generations.... [and] can be said to be 'inclusive' of modern science, although most Western scientists would go to great lengths to deny such inclusivity. (Cajete 2000, p. 3) As institutions, science and science education alike have rarely included the perspectives and contributions of indigenous peoples pertaining to the natural world. Yet, people worldwide have benefited from the traditional ecological knowledge of indigenous communities. Western science and technology, though broadly worthwhile, have been a source of global environmental damage (Wildcat 2009). Research has shown that indigenous ways of knowing can help students develop complex and multilogical understandings of the natural world (Aikenhead and Mitchell 2011; Cajete 1999; Chinn 2007; McKinley 2007). In particular, students can learn from native knowledge systems how to live in more sustainable ways (Kincheloe and Steinberg 2008; Wildcat 2009). In this article, we describe a lesson on climate change that explored possibilities for a more multilogical science education. Ninth- and tenth-grade science students investigated collaborations between Inuit elders and Western scientists working to understand how climate change alters bird migration patterns. The lesson connects to the Next Generation Science Standards (NGSS Lead States 2013) and the nature of science (see box, p. 34). We conclude by discussing possibilities for integrating indigenous knowledge in science education. Comparing sea ice and observations To begin exploring how indigenous people and Western scientists collaborate to understand natural phenomena, class started with a quick activity focused on Inuit understandings of sea ice. Using the Inuit siku (sea ice) atlas (see On the web), I, the first author, created 15 cards with either pictures or descriptions of different sea ice conditions (see On the web). Cards with pictures were separated from the cards with descriptions, which also had the Inuit term for each condition. Working in groups, students tried to pair pictures with their correct term and description. Students then shared their experience with the class. Many noted how difficult the task was and were surprised there were so many different kinds of sea ice. We discussed the many ways of knowing about a natural phenomenon and how traditional ecological knowledge (or indigenous ways of knowing) represents a highly complex system for documenting (in writing or orally), sense of, and responding to natural events. We then prepared to explore how climate change affects ecosystems using bird populations as a case study, drawing from both Inuit and Western science knowledge and practices. Students were first asked to share their own observations of birds and bird behaviors. Since birds are ubiquitous, students living almost anywhere can draw on their personal experiences to connect with the lesson. During initial discussion, students described seeing birds seeking food, eating at bird feeders, and swarming a hawk. We then introduced the driving questions for this activity: How do we learn about changes in nature? and How can changes in climate affect an ecosystem? Students wrote in their science notebooks their initial ideas about the questions, including making observations, taking pictures to compare how things have changed, and asking people what things were like in the area a long time ago. Then, we provided students (divided into groups of three or four) a set of observational scenarios including both indigenous and Western science observations related to temperature patterns and birds. The indigenous observations were made by Arctic groups, including the Inuit, Inuvialuit, Yupik, and Saami, and were selected from Krupnik and Jolly (2002) and Huntington et al. (2005). Their qualitative observations addressed changes in the climate and how these changes affected animal migrations and the ability of the elders to predict the weather over time (Figure 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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,019
Score d'incertitude au seuil0,065

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0000,000
Études des sciences et des technologies0,0040,002
Communication savante0,0020,004
Science ouverte0,0010,005
Intégrité de la recherche0,0030,006
Charge utile insuffisante (le modèle a refusé de juger)0,0190,004

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 Opus0,284
Tête enseignante GPT0,440
Écart entre enseignants0,156 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
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

Citations12
Publié2016
Routes d'admission1
Résumé présentoui

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