Supporting Teachers to Work with Children with Exceptionalities
Bibliographic record
Abstract
The current study had two purposes: to explore further revisions to the Three-Part Schedule D Additional Qualification (AQ) courses in special education and to determine if a virtual knowledge network would be a viable and welcome tool in building teacher capacity for classroom inclusion of students with exceptionalities. Educational stakeholders convened at the Ontario College of Teachers (OCT) for a two-day consultation meeting. A consensus-building workshop was used on the first day to discuss further revisions to the revised AQ course guidelines and to specifically identify gaps in teacher knowledge and skills. An open space consultation (Owen, 1997) was used on the second day to discuss the possibility of a provincial virtual knowledge network that would support revised Special Education AQ course guidelines and build capacity for teachers working with children with exceptionalities. Keywords: Ontario, teacher education, inclusive education, special education, additional qualifications.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.001 | 0.002 |
| 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".