A Transformative Investigation of an Inclusive and Positive IEP Framework
Bibliographic record
Abstract
In British Columbia, inspired and guided by Shelley Moore and her inclusionary and consultative efforts, a working group of school districts piloted a competencybased Individual Education Plan (IEP).This new IEP is to support the inclusion of students into a positive learning experience.Five years after this implementation phase and the acceptance of this IEP within the BC provincial system, a mixed-method study can investigate what changes are noticed when a competency-based IEP is implemented, who notices these changes, and do these changes extend beyond the IEP design phase.The transformative approach will best investigate these phenomena as the IEP is to support students from kindergarten to grade twelve who require additional support through special targeted funding.Student's voice is central to the competencybased IEP and can be central to this investigation.Care, therefore, for the student participants and centralizing their voice is essential within the study.Awareness on the part of the researcher will be critical to creating the space for student voice.The transformative approach can investigate whether a competency-based IEP promotes a more inclusive learning environment and/or a more positive learning experience.Findings indicating a more inclusive and positive learning experience deserve comprehensive knowledge mobilization so that more students within the kindergarten to grade twelve sectors may flourish.
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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 | 0.035 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.020 | 0.062 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".