How Indigenous and non-Indigenous ways of knowing, being, and doing might circulate together in science and mathematics education: A knowledge synthesis, 2006-2017
Bibliographic record
Abstract
This project reports on a knowledge synthesis examining ways in which Indigenous and non- Indigenous ways of knowing, being, and doing might circulate together in science and mathematics education, in both K-12 and teacher education. By combining elements of systematic reviews with Indigenous Research Methodologies, we identify bodies of work that have significant implications for researchers, educators, and policy members. An overview of this work focuses on foundational elements of relationship, place, and process in these bodies of work. We note relationships are central to the work with much of it emerging from formal and informal research partnerships that tend to predate collaborative research, which provides insights on both transformative potential and barriers that challenge transformation. With respect to place we note the emergence of regions with considerable work and gaps in geographical regions, grade levels, and publishing. Process plays a key role in the work that takes place, to strongly suggest that how the works occurs is as important as the content of the work. Four emerging themes provide implications for future work: 1) Culturally relevant education and ethical/cultural relationality; 2) Language; 3) Continual teacher learning/effort at all levels pre-service and in-service; and 4) Unlearning colonialism and decolonizing.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".