Bibliographic record
Abstract
In this paper, various dimensions of care involved in mathematics education are linked to the need to develop classroom activities that connect mathematics and social justice issues. Drawing from literature that shows cognition is situated and embodied, the importance of meaningful contextualization and social interaction when learning mathematics is highlighted. The concept of “simulation-based mathematics and social justice activities” is presented as an approach for the work of bringing social justice issues that have mathematics at their core to the classroom. Theoretical constructs and examples are discussed, to illustrate what such simulations may entail and what may be learned from scholars, in different educational fields, who use simulations in classroom activities. Potentially beneficial features of social justice simulations are related to various educational goals, such as: decreasing arbitrary boundaries between mathematical sub-areas and between mathematics and other disciplines; providing opportunities for choice and the embodiment of different perspectives; and offering opportunities for inter-personal learning. I report on a simulation-based mathematics and social justice activity, conducted in a teacher education classroom. Students—in this case future teachers—were prompted to write reflectively about their participation in the activity. My interest lies in finding out what, if any, cognitive and affective benefits these prospective teachers connect with their experience of mathematics in the activity. To investigate this, I analyze six themes that are present in the data and illustrate each theme using excerpts of student writing. The thematic analysis allows us to learn about connections made by these students, between mathematics, in the context of the activity, and issues that are personally meaningful to them, such as: their own future teaching practice, learning and interacting with peers, beliefs and feelings about mathematics and the learning of mathematics, and different perspectives on complex decisions, involving cooperation or lack thereof, that are encountered in real life situations.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".