Linguistic insights into dementia from 1994 to 2023: A structural topic modeling-assisted bibliometric analysis
Bibliographic record
Abstract
This article presents a bibliometric analysis of research on dementia in the field of linguistics. We reviewed and analyzed 545 articles published in 89 peer-reviewed journals between 1994 and 2023, to identify key bibliometric information and major research topics in this expanding field of research. The distribution of countries indicates that the United States is the most productive country, and researchers from the United Kingdom, Australia and Canada also play an important role. Aphasiology and Brain and Language are the most influential journals in terms of research productivity and impact. The analysis of highly cited references demonstrates the intellectual foundation of this research field. The topics generated by structural topic modeling show that scholars in linguistics have responded to a variety of issues on dementia, encompassing semantic processing, multilingualism and cognitive functions, primary progressive aphasia and apraxia of speech, natural language processing techniques, the role of speech-language pathologists, communication dynamics in contexts, speech processing, syntactic processing, and word retrieval and language processing. This study aims to enhance researchers’ understanding of the current state of this research field and provide insights for future studies.
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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 | 0.010 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.173 | 0.193 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".