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Record W7037607109

Exploring code switching in early number sense activities

2017· article· en· W7037607109 on OpenAlexfundno aff

Bibliographic record

VenueIslandScholar (University of Prince Edward Island) · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPasture and Agricultural Systems
Canadian institutionsnot available
FundersUniversity of Prince Edward Island
KeywordsRegister (sociolinguistics)Number senseChoseCode (set theory)Action (physics)Code-switchingRecallAction research
DOInot available

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.041
GPT teacher head0.218
Teacher spread0.177 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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