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Record W7018408374

Decolonizing STEM Learning through Land-Based Education in Ontario: The Generation of Guiding Principles and Discovery of Unanticipated Outcomes

2024· dissertation· en· W7018408374 on OpenAlexaboutno aff

Bibliographic record

VenueQSpace (Queen's University Library) · 2024
Typedissertation
Languageen
FieldSocial Sciences
TopicIndigenous and Place-Based Education
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousDecolonizationTraditional knowledgeProduct (mathematics)Process (computing)SPARK (programming language)
DOInot available

Abstract

fetched live from OpenAlex

In Canada Indigenous learners are underrepresented in STEM-related subjects and fields. This is problematic since because Western science, by design, prides itself on being value-free, it struggles to address moral questions regarding how nature should be treated. Combining Indigenous knowledge with the tools of Western science, however, has the potential to enable Western science to be applied in a manner that helps to both generate and maintain reciprocal relationships with the natural world. This dissertation reflects on the process through which diverse stakeholders worked together to produce decolonized grades 7-10 STEM resources intended to engage Indigenous learners in STEM subjects and reconnect Indigenous and non-Indigenous learners to the land. Results shared consider the role that land-based learning and land education play in the decolonization of STEM education. The research study also reflects on the effects of producing decolonized STEM learning materials on the community and institutional stakeholders involved in the process. Findings indicate that the process of working together to produce decolonized STEM learning materials, in addition to the product itself, can spark both personal and institutional shifts towards decolonization.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.275
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.034
GPT teacher head0.261
Teacher spread0.227 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2024
Admission routes1
Has abstractyes

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