Bibliographic record
Abstract
This article explores the efficacy of full inclusion from the perspective of students with exceptionalities, that is, those for whom special learning needs to have been identified, and compares perceptions of students in public schools with those of students in parochial schools. The study is part of a broader program of research, the purpose of which is to examine full inclusion, the prevailing model of special education service delivery, from the perspective of students and teachers, who are, in the final analysis, the primary stakeholders. The goals of full inclusion include the building of community in which all members are fully participating and accepted. These goals are certainly in keeping with Biblical teaching and so ought to be embraced by Christian schools. As a measure of the efficacy of inclusion in achieving the desired outcomes (including acceptance of and by peers) the study reported here obtained measures of overall self-concept and specific peer self-concept scores from students in four different schools: two public and two Christian. There were no significant between-school differences, but there were significant differences in peer self-concept between students with special needs and those with no identified special needs. Implications for educational practice and for teacher-education are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.017 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 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".