Lessons for Mathematics Education from the Practices of African American Mathematics Teachers
Bibliographic record
Abstract
In this commentary, we discuss the lessons we learned from case studies of two African American mathematics teachers, thereby endorsing the claim made by the contributors to this special issue that the insights they gained are not restricted to mathematics teaching in non-selective urban schools but can also inform the field more generally. We then focus on differences in the two teachers’ goals for students’ mathematical learning and clarify that they were consequential and constrained the types of purposes that the teachers could convey to their students for engaging in mathematical activity. We go on to argue that high expectations for all students’ learning are not by themselves sufficient for their development of mathematical proficiency and discuss the importance of supporting teachers’ development of specific instructional practices that enable their students to meet those expectations. Finally, we suggest that it is critical to situate the ways in which teachers draw on their cultural resources with respect to the school and district settings in which they work and in which they refine and elaborate their instructional practices.
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How this classification was reachedexpand
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".