Education For All:Approaches to Teacher Education for Inclusion
Bibliographic record
Abstract
Over the last thirty years, there has been an international aspiration to make education provision both inclusive and equitable with resultant policy production at both international and national level. Over time, the focus of this activity has moved from the specific needs of disabled students to consideration of how schools might celebrate diversity and provide effective learning for all students. Teacher education is viewed as a key factor in creating school environments where all young people have equity of access to relevant learning opportunities no matter their background or circumstances. This paper presents six case studies from Finland, New Zealand, Lithuania, Scotland, Norway and Canada charting the changes made over time to educational provision within their national context aiming to make schools more inclusive. Each case study highlights some of the ways in which teacher education has adapted in response to these policy changes to prepare new teachers to work in inclusive school settings. Common to all case studies is the identification that further research and change is required to meet the professional learning requirements of our future teachers. In response to this identified need. Highlighting the complex nature of providing inclusive education for all, it is suggested that future teacher education must continue to explore new ways to enhance the professional expertise of teachers to be inclusive of all learners in their daily practice.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.021 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".