Market “choices” or structured pathways? How specialized arts education contributes to the reproduction of inequality
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Located in one of the most diverse cities in the world, the Toronto District School Board (TDSB) offers several programs catering to a variety of student interests. Specialty Arts Programs (SAPs) have gained particular attention in part because of their reputation as excellent schools providing a unique opportunity for training in the arts. However, recently such programs have also raised concerns about who can access and who ultimately benefits from specialized programming in the arts. While the TDSB is committed to equal access for all families, the student populations at these programs do not mirror the broader school population, serving mostly affluent families and students with access to high levels of social and cultural capital. Employing data from the TDSB’s Parent and Student Census and the School Information Systems, the article first demonstrates the demographic homogeneity of specialized arts programs and then examines whether this homogeneity is a particular outcome of specialized arts programs or a manifestation of a de facto streaming mechanism that begins earlier in the schooling process. To do this, the authors explore the relationship between feeder schools and programs that guide students towards SAPs. Results demonstrate that the bulk of SAP students are drawn from a select few elementary schools across the board. Largely, the demographics of elementary feeder schools reflect similar characteristics of the SAP population and this relationship is amplified as the number of students drawn from feeder schools increases. In addition, students in SAPs experience a high level of belonging in school as compared to students across the system. While this outcome is often attributed to the immersion in arts-based curriculum, the authors query how the role of creating homogenous spaces through selective programming contributes to students’ experience of belonging while at the same time reproducing structural inequality.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it