Self-Advocacy Development in Ontario Adolescents with Learning Disabilities
Bibliographic record
Abstract
Learning disabilities (LDs) affect 3.2% of children in Canada and encompass a range of conditions impacting an individual’s psychological learning processes. Self-advocacy, an individual’s ability to speak up for themself, is a critical component of accessing supports and accommodations to manage LDs, especially as a child transitions throughout and beyond secondary education. This review will explore the development of self-advocacy in children with LDs through Ontario’s existing support and skill development programming, systemic barriers to accessing self-advocacy supports, as well as stigmatization towards LDs. Ontario’s current support infrastructure is a mix of resources that aim to increase students’ ability to navigate life with their LD. These aims align with well-documented barriers hindering students’ development of self-advocacy, such as attitudinal and societal factors, which stem from a lack of understanding surrounding LDs. Widespread stereotyping and stigma manifesting in depersonalization and social rejection further impact their relationship with their LD diagnosis and their overall perception of self. Overall findings highlight the need for research to gain a nuanced understanding of the gaps within Ontario’s current system to more effectively address the challenges that adolescents with LDs face in developing self-advocacy skills.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".