Indigenous Science Education: A Critical Conversation
Bibliographic record
Abstract
The purpose of this session is to engage educators and scholars in critical conversations on the topic of Indigenous Science Education, an important arena of educational inquiry where Indigenous and Euro-centric knowledge systems intersect and overlap. Indigenous Science Education has been identified variously as a driver of economic development for Indigenous communities (and beyond) and also as a potential source of creativity for the emergence of more equitable and sustainable ways of living. The hosts of this roundtable bring perspectives from Indigenous knowledge, complexity science, and anti-racism, and will encourage participants to weave their own thoughts and experiences related to Indigenous Science Education into the discussion. Possible questions and topics for conversation include: What is Indigenous Science Education? Is “Indigenous Science Education” an appropriate term? Complexity science as a bridge between Indigenous knowledge and Euro-centric Science Pedagogies of Indigenous Science Education (e.g., inquiry, experiential, and land-based approaches), and Becoming an ally through Indigenous Science Education. Please consider joining us to connect with others interested in Indigenous Science Education for some engaging and enlightening conversation.
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 | 0.058 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.077 | 0.069 |
| Scholarly communication | 0.027 | 0.037 |
| Open science | 0.006 | 0.033 |
| Research integrity | 0.027 | 0.050 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".