Asset-Based Approaches to Equitable Mathematics Education Research and Practice
Bibliographic record
Abstract
In July 2017, the National Council of Teachers of Mathematics (NCTM) released a new mission statement that shifts the organization's primary focus to supporting and advocating for the highest quality mathematics teaching and learning for all students. A key strategy for achieving this goal is to advance “a culture of equity where each and every person has access to high quality teaching and is empowered as a learner and doer of mathematics” (NCTM, 2017, “Strategic Framework,” para. 2). Increasing equity and ensuring the highest quality mathematics teaching and learning for all students requires systemic change (National Council of Supervisors of Mathematics [NCSM] & TODOS: Mathematics for ALL, 2016). As educators are called to enact NCTM's new mission, we acknowledge that such change is complex. We also acknowledge that our own experiences conducting equity work that is grounded in an asset-based approach are at different stages of development, ranging from beginning levels to lived experiences as diverse mathematics learners and mathematics education researchers. We see this change in mission as a call to both act politically (Aguirre et al., 2017) and to change story lines (i.e., “broad, culturally shared narrative[s]”; Herbel-Eisenmann et al., 2016, p. 104) that dominate the public perception of mathematics learning and teaching. We acknowledge that systemic barriers are part of a larger educational issue, but for the purposes of this commentary, we focus on mathematics.
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 | 0.089 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.013 | 0.104 |
| Scholarly communication | 0.022 | 0.025 |
| Open science | 0.005 | 0.025 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 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".