<p>Investigating stratification, language diversity and mathematics classroom interaction</p>
Bibliographic record
Abstract
Research on the socio-political dimensions of language diversity in mathematics classrooms is under-theorised and largely focuses on language choice. These dimensions are, however, likely to influence mathematics classroom interaction in many other ways than participants’ choice of language. To investigate these influences, I propose that the notions ofheteroglossia, orders of indexicality and scale-jumping, can provide new theoretical tools with which to understand the links between classroom interaction and broader social patterns of marginalisation. To illustrate the utility of these ideas, I include some analysis of an episode observed in a sheltered elementary school second language mathematics classroom in Canada. Investigando la estratificación, la diversidad lingüística y la interacción en el aula de matemáticas La investigación sobre las dimensiones sociopolíticas de la diversidad lingüística en clases de matemáticas está poco teorizada y mayormente se centra en la elección de la lengua. Estas dimensiones, no obstante, probablemente influyen en la interacción en clase en otros modos distintos a la elección de la lengua. Para investigar estas influencias, propongo que las nociones de heteroglosia, órdenes de indexicalidad y salto de escala, pueden aportar nuevos instrumentos teóricos con los cuales comprender conexiones entre interacción del aula y patrones sociales de marginalización. Para mostrar la utilidad de estas ideas, incluyo los análisis de un episodio de una clase de primaria de matemáticas canadiense con instrucción en una segunda lengua.Handle: http://hdl.handle.net/10481/42389WOS-ESCI
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".