The Evolutionary Logic of Autism: Adaptive Traits, Genetic Trade-Offs, and the DCIDE Framework
Bibliographic record
Abstract
This repository contains the curated dataset, threshold file, and final network outputs used in the co-occurrence analysis of autism and evolutionary models. All files are provided with MD5 checksums for verification. Files included: pubmed-autismORau-set.txt (24.16 MB)The full deduplicated dataset retrieved from PubMed using the query autism OR AU. Records were processed through exact DOI/PMID matching and fuzzy title–author matching (95% token-sort similarity threshold). thr.txt (4.19 KB)The applied thesaurus/threshold file used to harmonize terminology and set network thresholds in VOSviewer. This file ensures reproducibility of the node and edge selections. 50.json (95.45 KB)The processed co-occurrence network file (VOSviewer JSON format) containing the final retained nodes and links after thresholding. This is the exact file used to generate the published figure. 50.png (3.66 MB)High-resolution image export of the final co-occurrence network, corresponding directly to the figure included in the manuscript.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.044 | 0.021 |
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".