Science language<i>Wanted Alive</i>: Through the dialectical/dialogical lens of Vygotsky and the Bakhtin circle
Bibliographic record
Abstract
Abstract Over the past two decades, science educators increasingly have become interested in the role of language in the learning of science and have drawn on the work of Bakhtin, among others, for understanding the dialogical nature of knowledge in a sociocultural framework. However, the nature of language and its relation to thinking have not substantially changed and, in many ways, are incommensurable with the cultural–historical, materialist dialectical underpinning of the original framing of the sociocultural and dialogical approaches in the theories of L. S. Vygotsky and M. M. Bakhtin and his circle (e.g., V. N. Vološinov). Most importantly, currently available analyses of science classroom talk do not appear to exhibit sufficient appreciation of the fact that words, statements, and language are living phenomena, that is, they inherently change in speaking. In this paper, I begin by working out the premise that language is a living phenomenon that changes in use. I then present two key insights on language‐in‐use that derive from the works of Vygotsky and the Bakhtin circle, which are used to develop a theoretical and methodological frame. These key insights and the theoretical aspects of this paper are exemplified with materials from a concept mapping session in a 12th‐grade physics course. The proposed model has considerable implications for theorizing the relation between classroom talk and formal written genres of expression, and gives rise to many new research questions. © 2014 Wiley Periodicals, Inc. J Res Sci Teach 51: 1049–1083, 2014
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.059 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| 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".