Bibliographic record
Abstract
This edition continues the conversation concerning inclusive education by adding several articles from international authors. Drs. Blankenship, Fore and Boon of the University of Georgia provide a review of the literature concerning the efficacy and perception of inclusion at the secondary level for students with mild disabilities. Dr. Tsafi Timor currently teaches English in the secondary schools in Tel-Aviv, Israel. Her article addresses the issue of including students with learning disabilities in secondary schools in Israel. Dr. Marie S. Farmer of Georgia College and State University focuses her research on the future expectations of students with mental retardation included in regular education classrooms. Dr. Seevers and Ms. Garcia survey general education teachers’ attitudes regarding the use of assistive technology by students with learning disabilities. Ms. Cam Cobb of the Toronto District School Board identifies the potential needs of within the Korean-Canadian community concerning students with special needs. She then describes a source of support that has been developed by the community itself. Dr. Miller, Garriott, and Mershon discuss the perceptions of placement by students in general education classrooms. The additional voices of the international community bring a new flavor and perspective to the conversation concerning inclusive education. We are delighted to have their voices as well as the voices of those here in the United States in this discussion of the needs of students with special needs.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.783 | 0.575 |
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".