Bibliographic record
Abstract
While there is a large body of literature on the subject of inclusion from a student’s perspective in terms of program delivery, little has been written about how minoritized parents are included in special education processes. This critical study examines how minoritized parents – those who are at times disadvantaged because of how they are differentiated within society – are included in and/or excluded from special education in the varying circumstances associated with this process. To delve into the parameters and implementation of special education identification, placement, and program delivery, I spoke with four minoritized parents and one minoritized youth engagement worker. Additionally, I examined codified policies and regulations, in order to consider how individuals interpret and shape the enactment of this policy within school cultures. In recording and coding the stories of minoritized parents, I have found that Ontario’s system of identification, placement, and program delivery presently leads minoritized parents to experience varying degrees of inclusion and/or exclusion. These degrees may be influenced by a number of circumstances, including how knowledge, language, positioning, and philosophy are presented. As outlined in this paper, Ontario’s Ministry of Education, along with school boards across the province, may pursue a number of different change avenues, and these paths will inevitably lead to different outcomes. While some paths may lead to conflict resolution and enriched inclusion, others may intensify situations of exclusion. Any sort of policy change that sets out to transform special education identification, placement, and program delivery along an Inclusion/Exclusion, Transparency/Opaqueness continuum would ultimately have to address a variety of complications. While the two general forces of larger social context and policy complications are addressed in the concluding chapter of the paper, the specific manner in which they materialize cannot be predicted with complete accuracy. Rather than articulating a detailed set of instructions to redesign policy, I hope to generate critical reflection and discussion on the matter of transforming Ontario’s special education model. If special education inclusion is to be enriched in Ontario, change is imperative.
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 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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.014 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".