Tinkering with/in the Multicultural Science Education Debate: Towards Positing An(Other) Ontology
Bibliographic record
Abstract
Abstract The purpose of this chapter is to address the ways in which ontology, as an absent presence, is always already (re)shaping science education. Particularly, this chapter uses and troubles Cobern and Loving’s reminder that attention to ontology is uncommon within the multicultural science education debate. As they call for a (re)consideration of how epistemology aligns with ontology, concluding that knowing nature through WMS is universal and “common sense”, an ethic of deconstructive tinkering—using concepts, categories, and constructs that are uncommon to the context of science education to explore that which is common—is employed herein. Latching onto the binary co-constitution of common and uncommon, and moments in which they vacillate as a lever to (re)open spaces of science education to other meanings (e.g., Indigenous science to-come), Cobern and Loving’s criteria of ontological alignment is unsettled, (re)situating their claim of “common sense” towards (re)opening the logics of the multicultural science education debate.
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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 | 0.009 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.060 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.009 |
| 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".