Linguistic insights into dementia from 1994 to 2023: A structural topic modeling-assisted bibliometric analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.048 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it