Bibliographic record
Abstract
Teaching is generally considered a complex practice that involves the constant and dynamic interaction between the teacher, the students and the subject matter. One of the main goals of most education reform initiatives has been to change teachers’ classroom practices. Most recent reform curricula focus on highlighting teacher practices that promote and evoke students’ understanding alongside the changes in content (Tirosh & Graeber, 2003). Changes to a teacher’s role that are included in the education reform movement call for more research in order to understand and theorise teachers’ classroom practices. In this paper, I will present patterns-of-participation (PoP) as a promising framework that aims to understand the role of the teacher for emerging classroom practices. Instead of relying on a traditional approach to understanding classroom practices by analysing teachers’ beliefs, this framework applies a participatory approach to look for patterns in the participation of individual teachers in many social practices at the school and in the classroom. Some of these practices are directly related to the teaching and learning of mathematics while others are not. And some of them relate to communities that are not actually present in the classroom or at the school. PoP views teachers’ social interaction in a certain community as a piece which is influenced by other pieces of social interactions. In every interaction, the ‘pieces’ shape a ‘fluctuating pattern' that shows the shifting impact of different, previous practices and the dynamic relations between them (Skott, 2010; 2011; 2013).
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.010 | 0.021 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".