Introduction: Shifting Perspectives from Universalism to Cross-Culturalism
Bibliographic record
Abstract
Debates in science education over multiculturalism and universalism have disputed whether or not non-Western cultures have systems of knowledge about nature that could be considered science (Stanley & Brickhouse, 1994; Siegel, 1997). The following three articles have moved beyond that debate by accepting that all systems of knowledge about nature are embedded in the context of a cultural group; that all systems are, therefore, culture-laden; and that science (Western science) is the system of knowledge about nature that is predominant in Western culture. For example, some cultures give high priority to authoritative storytelling and demonstration of expertise, while others may value authoritative script and trial and error as methods of transmitting its knowledge of nature. What is contested in these articles is how to position Western science so that it can inform and be informed by the nature-knowledge systems of other cultures. Also in question is the role that non-Western nature-knowledge systems should play in the school science curriculum.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".