Unfettering discussions about social justice: the role of conversational prompts in discussions about mathematics education for Indigenous students
Bibliographic record
Abstract
Abstract To increase possibilities for listening respectfully to Indigenous educators, there is a need to identify conversational prompts which are used to raise alternative views of social justice about mathematics education for Indigenous students. Using Nancy Fraser’s description of abnormal social justice, an analysis was made of transcripts from round table sessions, at an Indigenous mathematics education conference. This analysis identified a number of conversational prompts that enabled shifts from normal to abnormal discussions about social justice. Normal discussions exhibited assumptions in which mathematics was valued as a Western domain of knowledge; cultural examples could be used as vehicles to teach mathematics; and decisions about education for Indigenous students should be made by external authorities. In abnormal discussions, these assumptions were queried and alternative possibilities arose. The conversational prompts, which initiated this querying, occurred in a number of ways, including the telling of stories and the asking of questions that either directly or indirectly challenged normal justice discourses about Indigenous students’ learning of mathematics. Identifying conversational prompts can assist non-Indigenous mathematics educators, who wish to be allies, to challenge their own and others’ assumptions about normal social justice issues related to mathematics education for Indigenous students.
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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 | 0.053 | 0.151 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.017 | 0.018 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.007 |
| 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".