Systemic Barriers to Inclusion: Leadership Practices to Support Inclusion
Bibliographic record
Abstract
The education system continues to struggle to implement inclusive practices for neurodiverse students. Despite a shift in education from segregation to inclusion, many barriers still preclude inclusion from becoming a true practice of education. In Alberta, Canada, not only are neurodiverse students calling for equity in the classroom, but students from various cultural \nbackgrounds are entering schools, creating diverse and complex classrooms. Teachers attempt to provide individual learning opportunities for all students but as class complexity increases this becomes impractical (Alberta Teachers’ Association, 2021). Through the identification of systemic barriers that continue to hamper inclusive educational development, school leaders can \nfind solutions. Regarding special education classes Jenson (2018) believed “special education continues to promote attitudes of disability being tragic and undesirable, consequently further excluding and oppressing these students” (p. 54). Teacher perceptions and attitudes focus on medical label of the child and not the individual nature of the child. The use of standardized \ncurricula and assessments continue to be detrimental to inclusive education, particularly at the high school level (Jurado-de-los-Santos et al., 2021). Teacher and principal leadership, perspectives, and willingness to change practices are important for realizing inclusive education (Theoharis et al., 2016). An extensive literature review outlines promising practices for school \nleaders to undertake to beat the systemic barriers until a revolution happens within the whole education system.
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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 | 0.021 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".