Serious Play: Inflecting the Multicultural Science Education Debate Through and for (Socratic) Dialogue
Bibliographic record
Abstract
Abstract The purpose of this chapter is to differentially revisit themulticultural science education debate, a central curricular location that acts as both a potential entry point and problematic gate-keeping device for Indigenous science to-come, by inflecting it with a potentially less oppositional mode of meaning-making: serious play. Within this debate, it is generally agreed upon that there is a clear moral imperative to respect students from diverse cultural backgrounds within the multicultural science education classroom. However, what constitutes respect and how it is enacted continues to be hotly debated due to differing considerations of “what counts” as science. This has produced two largely incommensurable positions around the inclusion of Indigenous ways-of-living-with-Nature (e.g., ethnoscience, Indigenous science): those who contest its status as scientific knowledge and those who champion it. However, as the process of debate enacted is commonly one of opposition, there is little room for meaning-made across positions. Above and beyond addressing the sources of knowledge that continue to uphold this serious debate, this chapter plays with/in the debate processes as a means of opening these foreclosed spaces in science education as both form and content lead to the excluding, differing, and deferring of Indigenous science to-come.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.042 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".