A Bibliometric Analysis and Visualization Regarding Language Impairment Pertinent to Autism Based on VOSviewer and CiteSpace from 2014 to 2022
Bibliographic record
Abstract
Language impairment in autism is a vital focus of academic research. However, it is worth noting that there is still rare literature in the field of quantitative analysis and visualization to papers spanning from 2014 to 2022about language disorders in autism using the VOSviewer and Citespace methods. We carefully curated a final dataset of 422 publications with the goal of rigorous bibliometric analysis and visualization using VOSviewer and CiteSpace within the Web of Science Core Collection from 2014 to 2022 in terms of keyword analysis, bibliometric analysis of journals, organizations, counties, co-authors, co-cited references and keywords with citation bursts. The results of this research have shown that: Firstly, intellectual disability, developmental language disorder, and investigations concerning attention-related aspects have been prominent and evolving subjects within this dynamic research landscape; Secondly, the top three journals with the highest number of publications were Journal of Autism and Developmental Disorders, Frontiers in Psychology, and Autism Research; Thirdly, University College London (UCL) has the most significant number of articles, followed by University of Wisconsin Madison and University of Washington; Fourthly, the United States has the most significant number of articles, followed by England, Australia and Canada; Fifthly, the top three authors with the most publications were Weismer, Susan Ellis, Gillberg, Christopher and Lord, Catherine.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.008 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.171 | 0.137 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".