Exploring code switching in early number sense activities
Bibliographic record
Abstract
Previous research indicated that teachers have a fundamental role in teaching the proper\nlanguage of mathematics, also known as the mathematics register (Pimm, 1978). However, little\nis known about how educators use language to communicate and mediate mathematics during\nthe early years of schooling. This multiple case study explored three educators’ use of languge as\nthey taught number sense in their classrooms, as well as their views and understanding of\nmathematics education. During a month spent collecting data at each of the three sites, the\npreschool, kindergarten, and grade 1 educators were involved in an initial interview, five\nvideotaped teaching sessions, and a recall interview after the video sessions. Discourse analysis\nwas utilized to explore language use (Gee, 2014). Findings indicate that educators code switched\nto the mathematics register (a) when they talked about numbers, number words and counting; (b)\nto revoice students’ ideas; (c) to explain student and teacher actions; (d) to provide new math\ninformation; and (e) when they chose between two terms that belonged to the math register.\nFindings also demonstrated that educators viewed the language of mathematics as an outside\nentity and that they preferred to avoid the use of the mathematics’ register, and relied instead on\nprotoquantitative terms, everyday terms, and words that denoted action to communicate and\nmediate mathematics to young students.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".