Bibliographic record
Abstract
Today we begin in earnest the work of making sure that the world we leave our children is just a little bit better than the one we inhabit today. President Barack Obama Individuals involved in the education of children with special needs are concerned about the shortage of personnel in higher education in the field of exceptionality. This has been true for the past quarter century. In the early 1990s, researchers had noted that for over a decade, authorities in the field of special education have been shouting out loud about the shortage of, and need for, personnel in special education (Sindelar, Buck, Carpenter, and Wantanabe 1993; Smith and Pierce 1995). Calls for attention to this issue continue today. Wasburn-Moses (2008) stated: “despite the growing demand for professionals with doctoral degrees in special education, doctoral programs are not producing enough graduates to fulfill this need” (p. 259). Addition–ally, educators are equally concerned about the quality and design of doctoral special education programs across the United States. As the number of children with special needs continues to increase, school districts must respond to the needs of teaching and preparing these young individuals for society.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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; both teacher heads agree on what is shown here.
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".