Mapping and Analyzing the Scientific Outcomes in Autism Spectrum Disorder Using Lexical Co-occurrence Approach
Bibliographic record
Abstract
Introduction: Cohesion indicator is one of the scientific mapping tools which uses the most important words in documents to study the conceptual structure of a research area. The purpose of the present study was to analyze the structure of the scientific map of autism outputs through lexical co-occurrence analysis in the Clarivate Analytics Web of Science Database. Methods: This study was conducted using scientometric method. The research population consisted of 14186 autism-related records published between the years 2010 and 2017 at the Clarivate Analytics Web of Science Database. The data were analyzed using social network analysis method. Results: The words “ability, malformations, syndrome, disorder, phenotype, and neurons” were the main vocabulary in the domain of autism spectrum disorder. These words also received the highest score in terms of centrality factors. Furthermore, in terms of macro-indicators, the domain of autism was coherent. In this area, the United States, the United Kingdom, and Canada had produced more records compared to other countries. The universities of California, London, and Harvard had also been the most productive universities in the international arena. Among Iranian universities, Tehran University of Medical Sciences, Islamic Azad University, and Shahid Beheshti University of Medical Sciences had more publications compared to other universities. Among the top researchers in terms of number of international productions "Zwaigenbaum L.", "Matson JL." and "Gillberg C." and among Iranian researchers "Memari A", "Mashayedi P", and "Ahmadloo M" had the best works. Conclusion: The information extracted from lexical co-occurrence map can help to improve policy-making in scientific fields. In this map, each word or group of words represents a particular area. Therefore, these maps can be used to make efficient decisions regarding resource allocation and distribution. Furthermore, these maps can help researchers get acquainted with new topics and top researchers in each field.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.053 | 0.044 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".