Cultural diversity in science education through <i>Novelization</i>: Against the <i>Epicization</i> of science and cultural centralization
Bibliographic record
Abstract
Abstract Science educators are confronted with the challenge to accommodate in their classes an increasing cultural and linguistic diversity that results from globalization. Challenged by the call to work towards valuing and keeping this diversity in the face of the canonical nature of school science discourse, we propose a new way of thinking about and investigating these problems. Drawing on the work of Mikhail Bakhtin, we articulate epicization and novelization as concepts that allow us to understand, respectively, the processes of (a) centralizing and homogenizing culture and language and (b) pluralizing culture and language. We present and analyze three examples that exhibit how existing mundane science education practices tend, by means of epicization, towards a unitary language and to cultural centralization. We then propose novelization as a way for thinking the opening up of science education by interacting with and incorporating alternative forms of knowing that arise from cultural diversity. © 2011 Wiley Periodicals, Inc. J Res Sci Teach 48: 824–847, 2011
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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 | 0.014 | 0.011 |
| 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 | 0.061 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".