There is No Choice: Examining Somali Parents' Experience with Special Education
Bibliographic record
Abstract
We now have a student population that is culturally, linguistically and economically diverse. This, in turn, has resulted in school board initiatives in response to this diversity, primarily; it appears, in ways that harden social class and racial divisions in the society (Roberston & Kushner, 2006). One clear example of this is the over-representation in Special Education of poor, immigrant students of colour, whose culture and language differs from that of English-speaking Canada. This thesis focuses on the experience of Somali parents whose children have been placed in Special Education or where an attempt has been made to place them. The purpose of this study is to examine if high or low cultural capital among Somali parents’ influences the placement of their children in Special Education. Through qualitative semi-structured interviews, I look at the experience of 8 Somali parents. Thematic analysis of the data is used to understand the findings.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.047 | 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".