Inclusive Postsecondary Education—An Evidence‐Based Moral Imperative
Bibliographic record
Abstract
Abstract Although today many more examples of postsecondary educational opportunities being made available to students are expanding for adults with intellectual disabilities ( ID ), the majority of these opportunities are either segregated or partially segregated with few accommodating students with significant disabilities or challenging behaviors. In this article, the authors take the position that the desire for inclusive education and the beliefs and principles of inclusive practices must be the foundation for inclusive postsecondary education ( IPSE ). The rationale for such an approach is based on positive outcomes derived for young adults where opportunities for inclusion in the context of universities, colleges, and technical schools offer a powerful context for embedding students in the normative pathways that can lead to positive lifelong outcomes. As inclusive schooling remains a controversial issue even after 40 years of supportive published research and demonstrated practice, it is not surprising that full IPSE opportunities are limited. The authors hold to the principles of inclusion as the foundation for postsecondary education given the known failure of segregated education to result in positive social and economic outcomes. The authors explore the means of achieving better futures for students with ID through IPSE . This article highlights the findings of 25 years experience across the province of Alberta in implementing 18 IPSE initiatives for young adults with the full range of ID , including those with severe and multiple disabilities, and outlines the challenging behaviors thus strengthening evidence for adopting inclusive practices. The supports required for an authentic student experience in all aspects of postsecondary academic and social life are described with employment, academic, economic and social outcomes highlighted. IPSE has been shown to be an important and effective means of launching students with ID into adulthood, but by itself, IPSE is not sufficiently powerful to sustain an inclusive pathway over time. The authors note that student experiences in campus life and relationships reveal we are not close to finding the limits to where and how inclusion can be achieved; the challenge is to create opportunities.
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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 | 0.126 | 0.253 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.020 | 0.010 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.012 | 0.025 |
| Insufficient payload (model declined to judge) | 0.006 | 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".