Exploring the frontiers of eye tracking research in language studies: a novel co-citation scientometric review
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.
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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 | 0.053 | 0.191 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.127 | 0.131 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".