Self-knowledge as a Potential Tool for Inclusive Education: Using a Case Study to Explore Self- Including Strategies
Bibliographic record
Abstract
The goal of this study was to discover the self-including strategies among students with special learning needs in general education settings. An exploratory case study method and purposeful sampling were employed to render an in-depth understanding of the mechanism between self-knowledge and self-inclusion. We adopted Glaser’s coding approach for data analysis and administered member checking and peer briefing for ensuring descriptive accuracy and enhancing the validity of the findings. In addition to a series of interviews of the unique case identified for the study, we interviewed teachers and a parent for information triangulation. The findings show that self-inclusion is a complex process that involves both knowing and doing. One starts with constructing self-knowledge, fueling the urge for initiating advocacy actions, which in turn strengthen the self-knowledge formation. Three self-including strategies for Self-knowledge Construction are (1) Collecting Self-related Information, (2) Equipping with Learning Skills, and (3) Validating Academic and Social-emotional Competence. Two self-including strategies for Advocacy Actions are (1) Advocating for Self and (2) Advocating for Others. The data also suggests Direct Supports and Personal Intelligences as constant contributing factors to the self-inclusion process. The study will lend further credence to the development of curriculum and psychometric scales.
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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 | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".