Bibliographic record
Abstract
This special issue provides a selective overview of topics associated with a pre-dominant trend in Canadian and U.S. schools: moving from a dual system of education in which special education and regular education services are carried out separately, to an effective unified system of service delivery for all students (Stainback, Stainback, & Bunch, 1989). Even though much of the impetus for change has come from proponents in special education (Lipsky & Gartner, 1989; Porter & Richler, 1991; Villa, Thousand, Stainback, & Stainback, 1992), there is increasing evidence that general education reform and school improvement agendas are beginning to take hold (Barth, 1991; Smith & Scott, 1990). The chal-lenge of creating school environments that promote excellence and equity is daunting but not impossible, and some of the preliminary efforts in this area are very promising. The focus of this special issue is on some of the significant work that is being carried out across the country to support this change. Although the range of topics is diverse, all papers are concerned with the complex problems associated with providing every student (particularly students with exceptional learning needs) an appropriate education that enables each to reach maximal potential.
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 | 0.021 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.012 | 0.016 |
| Scholarly communication | 0.042 | 0.047 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.014 | 0.022 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".