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Record W3037501181 · doi:10.54870/1551-3440.1517

Indigenous Culture-Based School Mathematics in Action Part II: The Study’s Results: What Support Do Teachers Need?

2021· article· en· W3037501181 on OpenAlexaff
Sharon Meyer, Glen S. Aikenhead

Bibliographic record

VenueThe Mathematics Enthusiast · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicMathematics Education and Teaching Techniques
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsIndigenousAppropriationMathematics educationIdeologyPerspective (graphical)Action (physics)Value (mathematics)Inclusion (mineral)Action researchPedagogySociologyMathematicsEpistemologySocial sciencePolitical scienceBiologyEcologyGeometry

Abstract

fetched live from OpenAlex

This second part of two related articles reports the answers to the research question: What precise supports must be in place for Grades 5 to 12 teachers to enhance their mathematics classes in a sustainable way with Indigenous mathematizing and Indigenous worldview perspectives? In addition to various logistical supports, two other types of supports were identified: supports for learning and unlearning ways of perceiving the world generally and perceiving Western mathematics specifically. These needed supports came to light when we mentored the teachers. On the one hand, the co-researching teachers learned, or had already learned: (a) the plurality of mathematical systems; (b) the perspective of Western mathematics as a human endeavor along with its values, ideologies, and definitions; (c) the mere inclusion of Indigenous mathematizing in a lesson is not enough; and (d) the goal of two-eyed seeing. On the other hand, the co-researching teachers unlearned, or had already unlearned: (a) pure mathematics’ claim to be value-free, (b) all students have a predilection to excel at mathematics, and (c) subtle appropriation committed by many mathematics educators as if it were common sense to do it.

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)0.006
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.230
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.060
GPT teacher head0.361
Teacher spread0.300 · 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.

Study designQualitative
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

Citations8
Published2021
Admission routes1
Has abstractyes

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