Bibliographic record
Abstract
Contents: B.K. Koegh, D.P. Hallahan, Introduction. D. Johnson, B.K. Keogh, The Lives and Careers of Bill and Dorothy Cruickshank. M.Y. Kibby, G.W. Hynd, Neurological Basis of Learning Disabilities. R. Kotkin, S. Forness, K.A. Kavale, Comorbid ADHD and Learning Disabilities: Diagnosis, Special Education, and Intervention. D. Sridhar, S. Vaughn, Social Functioning of Students with Learning Disabilities. L.S. Fuchs, D. Fuchs, Using Assessment to Account for and Promote Strong Outcomes for Students With Learning Disabilities. L. Meltzer, M. Montague, Strategic Learning in Students With Learning Disabilities: What Have We Learned? M.P. Weiss, J.W. Lloyd, Structure and Effective Teaching. J.B. Crockett, J.M. Kauffman, The Concept of the Least Restrictive Environment and Learning Disabilities: Least Restrictive of What? P.J. Gerber, Learning Disabilities: A Lifespan Approach. J. Elkins, Learning Disabilities in Australia. B.Y.L. Wong, N. Hutchinson, Learning Disabilities in Canada. G. Opp, Learning Disabilities in Germany. K. Wedell, British Orientations to Specific Learning Difficulties. M. Tsuge, Learning Disabilities in Japan. L.M. Stevens, W. van Werkhoven, Learning Disabilities in The Netherlands. I. Lundberg, T. Hoien, Reading Disabilities in Scandinavia. L. Bravo-Valdivieso, N.M. Muller, Learning Disabilities Studies in South America. S. Vogel, S. Reder, A Comparative Study of Self-Reported Learning Disabilities: Findings From the International Adult Literacy Survey.
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 | 0.001 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".