Enhancing Students' Mathematical Learning Through Focused Teacher Growth: A Review of of Promoting Purposeful Discourse…
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Communication within the mathematics classroom has captured the interest of mathematics educators over several decades. The National Council of Teachers of Mathematics Standards publications (1989, 1991, 2000) highlight communication as one of the fundamental strands in mathematical processes. Although research has investigated students' written mathematics work (e.g., Masingila & Prus-Wisniowska, 1996; Mason & McFeetors, 2002; Pugalee, 2004), considerable focus has also been given to understanding effective spoken discourse patterns within the mathematics classroom (e.g., Hufferd-Ackles, Fuson, & Sherin, 2004; Lampert & Blunk, 1998; Nathan & Knuth, 2003). Pimm (1994) argues that focusing on “the form and structure of spoken interactions between mathematics teachers and pupils” (p. 134) can inform the way in which classroom discourse is shaped. He encourages the use of discourse analysis as one way of making sense of questions that address the what, how, and why of teachers' forms of language in teaching mathematics. Increasingly, studies using discourse analysis are being used to describe effective classroom communicative practices (e.g., Bills, 2000; Gresalfi, Martin, Hand, & Greeno, 2009; Truxaw & DeFranco, 2008; Zolkower & Shreyar, 2007).
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.042 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it