A transdisciplinary team approach to achieving moral agency across regular and special education in K‐12 schools
Bibliographic record
Abstract
Purpose The purpose of this paper is to propose a framework for integrating social responsibility within the accountability context now prevalent across the regular and special education contexts of Canadian and American schools while exposing readers to many of the different theories that exist concerning transdisciplinary forms of inclusive education. Design/methodology/approach The author uses her experience as superintendent to create a system of inclusive and authentic collaboration amongst educators, parents, and specialists in the hope of creating a more complete plan for special education in her district. She introduces these collaborative teams to numerous various theoretical frameworks hoping to expand their views of what constitutes “acceptable” educational knowledge. Findings Results from the full‐scale implementation of the new transdisciplinary model indicate that emergent collaborative sensibilities among team members are beginning to characterize educational work which reflects a transition towards a more socially responsible learning community characterized by qualities of transparency, honesty, inclusivity, interdependence, respectful reciprocity, trust, and caring. Originality/value The study furthers our understanding of special education programs and the different ways in which it is possible to improve the current special education system. The study introduces us to one specific study (related to special education and done by the author herself) while continuously relating that study to grounded and established educational and ethic‐related theory.
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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 | 0.024 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.028 | 0.030 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".