“Nobody’s Rich and Nobody’s Poor … It Sounds Good, but It’s Actually Not”: Affluent Students Learning Mathematics and Social Justice
Why this work is in the frame
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Bibliographic record
Abstract
AbstractThis article investigates how affluent students made sense of social justice issues that were embedded in mathematics learning activities. I present 2 case studies of such activities at the intermediate and secondary levels in 2 different schools. The analysis draws on video records and classroom artifacts and applies the theoretical framework of figured worlds to consider how students drew on their past experiences and on the structure of the classroom activities to understand the mathematics and the social justice issues. The analysis demonstrates how the 1st activity provided a familiar figured world to support learning about issues of wealth distribution. In the 2nd activity, because of a lack of what are termed intermediary figured worlds, students were left to draw on only their own experiences and background knowledge, including stereotypes about poor neighborhoods. ACKNOWLEDGMENTSThe material presented in this article was adapted from a paper presented at the 2011 annual meeting of the Jean Piaget Society in Berkeley, California. The research was supported in part through funding from the Knowles Science Teaching Foundation. I would like to thank Jennifer Langer-Osuna, Joseph Flessa, Jessica Thompson, Tesha Sengupta-Irving, Paula Hooper, and my research team (Jennifer Calix, Lesley Dookie, James Eslinger, Stephanie McKean, and Miwa Takeuchi) for helpful feedback during the writing of this article. I would also like to thank the teachers and students who participated in the action research project for their generosity in inviting me into their classrooms and for further developing my understanding of teaching mathematics for social justice.Notes1James Gee (Citation1996) made a similar point when he contrasted discourses that are acquired in the home through interaction and those that are learned in more formal educational settings. According to Gee, acquisition does not require explicit instruction, whereas learning does.2Pseudonyms are used throughout, except in cases when it was not clear who the speaker was.3Transcription conventions are adapted from those used in conversation analysis. In particular, [ ] is used to indicate overlapping talk.4It is possible to argue that there were actually two community figured worlds involved in this activity: the affluent neighborhood and the high-poverty neighborhood. Because the teacher and students did not openly and meaningfully distinguish between the kinds of characters and storylines found in each type of neighborhood, I follow their reasoning and consider neighborhood life to be a single figured world.5In fact, many high-poverty communities would prefer less, not more, police presence, as they feel that police violence is mainly directed toward community members.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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