The Study of EDA in China during 2013–2024: Current Status, Development, and Prospects
Bibliographic record
Abstract
Ecological discourse analysis (EDA), an interdisciplinary field within the ecological framework, aims to uncover the role of language in shaping ecosystems. It explores the construction of the human—ecosystem relationship through language and examines the emergence and propagation of speciesism and anthropocentrism. This study comprehensively reviews and deeply analyzes China’s EDA research from 2013 to 2024. Relying mainly on the CNKI database and using the CiteSpace analysis method, it examines multiple aspects, including research trends, corpus types, research perspectives, and authors’ contributions. China’s EDA research has shown a steady growth trend. Scholars have applied it in various fields such as literature, tourism, and news media, using diverse corpora like translations of classical Chinese poetry, English poems, and news reports. Research approaches like harmonious discourse analysis and Systemic Functional Linguistics (SFL) have been widely employed. Nevertheless, several challenges remain. There is a shortage of quantitative research, insufficient exploration of discourse dissemination among social groups, and a delay in emerging media—related studies. For future research, it is essential to prioritize quantitative methods, focus on social—group differences, keep up with emerging media trends, strengthen interdisciplinary cooperation, integrate global and local perspectives, and monitor the dynamic changes in ecological discourse. This not only enhances China’s academic influence in this field but also promotes ecological protection by raising public awareness of the complex language—ecosystem relationship. Moreover, it can facilitate cultural exchange and understanding by comparing and integrating ecological discourses globally, enriching cultural diversity. In education, such research can offer valuable insights for curriculum design, helping students better understand ecological issues and fostering environmental responsibility from a linguistic perspective.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 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".