Principals’ Roles and Responsibilities in Creating Inclusive School Environments for People with Disabilities
Bibliographic record
Abstract
Ontario education providers have a responsibility to accommodate the needs of their students and employees with disabilities (Ontario Human Rights Commission, 2008). Under the 2001 Ontarians with Disabilities Act, the Ministry of Education along with school boards are required to prepare, update, and make public accessibility plans that address the identification, removal, and prevention of barriers for people with disabilities (Valeo, 2010). However, in 2012, physical, architectural, and attitudinal impediments frequently fail to meet basic accessibility and equity standards. The purpose of this paper is to juxtapose my own personal narrative as a disabled teacher candidate, with literature about the roles and responsibilities of principals and administrators in creating inclusive school environments for people with disabilities. Administrators’ roles in leadership, mediation, and collaboration, crucially influence the success of inclusive school organizations (Goddard & Hart, 2007; Irvine et al., 2010; Ross & Berger, 2009; Valeo, 2010), and directly affect the experiences of individuals therein. This paper highlights the need for principals to be knowledgeable about disability issues such as recognizing barriers, using preventative instead of reactive strategies toward physical disability concerns, and addressing challenges administrators face when trying to create integrative, inclusive school environments.
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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 | 0.014 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.028 | 0.020 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".