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Record W2588234694 · doi:10.2505/4/tst16_083_04_31

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

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

Bibliographic record

VenueThe Science Teacher · 2016
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousTraditional knowledgeScience educationNatural (archaeology)Climate scienceNinthEnvironmental ethicsClimate changeSociologyGeographyEcologyPedagogyArchaeology

Abstract

fetched live from 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). …

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0040.002
Scholarly communication0.0020.004
Open science0.0010.005
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0190.004

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.284
GPT teacher head0.440
Teacher spread0.156 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations12
Published2016
Admission routes1
Has abstractyes

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