Bibliographic record
Abstract
New Brunswick's full inclusion model aims to ensure that all students receive the necessary supports to access their learning and participate fully in school life. While inclusive education is praised for its espoused principles, challenges persist that hinder its implementation. The prevailing support-oriented approach often focuses on students' challenges rather than their strengths. Recent perspectives have emphasized the need to move beyond a primarily support-oriented model to incorporate a more strengths-focused view of students' learning. Positive education, linking academic achievement to wellbeing, leverages such approaches. This chapter explores a dual-dimensional model for inclusive student learning through investigating existing support-oriented and strengths-based approaches within New Brunswick's school using an Appreciative Inquiry research design. It concludes by introducing the Strength and Support Response Model (SSRM), which reimagines the Response to Intervention (RtI) framework by integrating and leveraging students' strengths into support structures.
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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 | 0.040 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".