Bibliographic record
Abstract
Introduction. 1. The epidemiology of pervasive developmental disorders, Eric Fombonne, Canada. 2. Early manifestation of autistic disorder during the first two years of life, Juan Martos Perez, Pedro M. Gonzalez, Maria Llorente and Carmen Nieto, Spain. 3. Early assessment in autism, Catherine Lord, USA. 4. Implicit learning impairments in autism: Implications for diagnosis and treatment, Laura Klinger, Mark Klinger and Patricia Pohling, USA. 5. Joint attention and autism: Theory, assessment and neurodevelopment, Peter Mundy and Danielle Thorp, USA. 6. On being moved in thought and feeling: An approach to autism, Peter Hobson, UK. 7. Systemising and empathising in autism, Sally Wheelwright, UK. 8. Executive functions in autism: Theory and practice, Sally Ozonoff, USA. 9. Relationship between language and development in autistic spectrum disorders, Isabelle Rapin, USA. 10. Developmental and behavioural profiles of children with autism and Asperger Syndrome, Susan Leekam, UK. 11. Neuro-anatomical observations of the brain in autism, Margaret Bauman and Thomas L. Kemper, USA. 12. Cortical circuit abnormalities (minicolumns) in the brains of autistic patients, Manuel F. Casanova, USA. 13. Genetic research into autistic disorder, Angel Diez Cuervo, Spain. 14. Parents and professionals. Collaboration! Collaboration? Hilde De Clerk and Theo Peeters, Belgium. References. Subject index. Author index.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.044 | 0.010 |
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".