Visualising the Knowledge Domain of Linguistic Landscape Research: A Scientometric Review (1994-2021)
Bibliographic record
Abstract
Linguistic landscape (LL) is a prism that reflects the linguistic dynamics, language policies, and power relations in given territories. By utilising the scientometric software, CiteSpace 5.8.R3, this paper provides a visualised overview of 654 records and 19746 references (1994-2021) on LL selected from the Web of Science Core Collection (WoSCC). The scientific network analysis, keyword network analysis, and co-citation analysis were undertaken. The leading authors, institutions, and countries in the LL field were identified through scientific network analysis. Analyses of high-frequency keywords and the cluster analysis of keywords identified the hot topics within the LL domain. Document co-citation analysis and co-cited reference clusters were determined to examine influential works and LL research frontiers. The findings indicated that LL research had been expanded from the initial focus on identity and language policy to today’s in-depth explorations of language, multilingualism, and English in the globalisation context. In addition, the research object and research approach have made a critical turn to a highly interdisciplinary way.
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.
How this classification was reachedexpand
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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 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 teacher head, 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".