Exploring the frontiers of eye tracking research in language studies: a novel co-citation scientometric review
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
Eye tracking technology has become an increasingly popular methodology in language studies. Using data from 27 journals in language sciences indexed in the Social Science Citation Index and/or Scopus, we conducted an in-depth scientometric analysis of 341 research publications together with their 14,866 references between 1994 and 2018. We identified a number of countries, researchers, universities, and institutes with large numbers of publications in eye tracking research in language studies. We further discovered a mixed multitude of connected research trends that have shaped the nature and development of eye tracking research. Specifically, a document co-citation analysis revealed a number of major research clusters, their key topics, connections, and bursts (sudden citation surges). For example, the foci of clusters #0 through #5 were found to be perceptual learning, regressive eye movement(s), attributive adjective(s), stereotypical gender, discourse processing, and bilingual adult(s). The content of all the major clusters was closely examined and synthesized in the form of an in-depth review. Finally, we grounded the findings within a data-driven theory of scientific revolution and discussed how the observed patterns have contributed to the emergence of new trends. As the first scientometric investigation of eye tracking research in language studies, the present study offers several implications for future research that are discussed.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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