Science, Culture and Citizenship: Cross-Cultural Science Education
Bibliographic record
Abstract
My paper has three purposes: (1) to explore an alternative to the conventional mono-cultural science curriculum in schools narrowly defined by Eurocentric science; (2) to consider the benefits that accrue from a school science curriculum that recognizes the knowledge of nature held by an Indigenous culture as being foundational to understanding the physical world; and (3) to illustrate this cross-cultural school science by what we are accomplishing in Saskatchewan, Canada. From an anthropological perspective, science can be seen as anchored in Euro-American cultures (i.e., Eurocentric science), regardless of the cultural identities of non-Euro-American professional scientists. The vast majority of students experience school science as a foreign culture, but their teachers do not treat it that way. Culture clashes for socially marginalized students in society (e.g., Indigenous students) are particularly pronounced. Conventional school science discriminates against their culture’s way of knowing nature and alienates many of them in science classrooms. A cross-cultural school science, on the other hand, does not accept the hegemony of Eurocentrism, but instead seeks ethical, social, ecological, and economic rewards for all students and citizens as a consequence to implementing a cross-cultural curriculum that recognizes Indigenous knowledge as being foundational to understanding nature.In the province of Saskatchewan, Canada, we are implementing a science curriculum that introduces some Indigenous knowledge of nature into conventional school science. The provincial school science curriculum is now a pluralistic curriculum that stipulates content to be studied from two knowledge systems (Eurocentric and Indigenous). Eurocentric-Indigenous, cross-cultural, science curricula need to be developed in countries with a history of colonization. Implementation involves science teachers who build cultural bridges between their Eurocentric science culture and a local Indigenous culture.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.022 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".