Bibliographic record
Abstract
The purpose of this study is to determine the reason for rapidly escalating diagnoses of autism. In 2018, the Centers for Disease Control (CDC) reported that 1 in 44 children were diagnosed with an autism spectrum disorder, for a prevalence rate of 2.27% of the population. In 2012, a review of global prevalence of autism found 62 cases per 10,000 people, for a prevalence rate of 0.62 percent. This apparent 266 percent increase in autism prevalence is in stark contrast to all other disorders listed in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), for which there has been no increase in prevalence over this same six-year period. The increase in alleged autism prevalence from 0.62 to 2.27 percent is entirely due to the DSM-5 creation of a false and overly broad autism spectrum (ASD) catch-all category that includes conditions unrelated to autism. These figures suggest that 70% of those who have been given an ASD diagnosis may not be autistic. What the psychology professions urgently require is a causal based definition of autism, as recommended in this report. Autism is an inherent neurophysiological difference in how the brain processes information and is caused by a dysfunctional cingulate gyrus (CG), that part of the brain which focuses attention.
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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".