Bibliographic record
Abstract
This article aims to analyze the research trend and hotspots in the field of comparative literature by using the method of bibliometrics. The data is derived from the Web of Science Core Collection Database. The visualization software VOSViewer is utilized to draw keyword co-occurrence knowledge graph. R programming language is employed to analyze the quantity of publications, core journals, highly cited papers, the most contributing authors, and keyword word cloud. The results indicate that ever since 1975, this study field has entered a period of rapid development; top journals with most publications are mainly from France, the United States, the United Kingdom and Canada. Most of the highly cited articles have emerged in the recent two decades, and quite a few of them inherit the academic tradition of adopting a geographic perspective. The keyword word cloud and the keyword co-ocurrence knowledge mapping reveal that comparative literature study is shifting its focus from literary history and intertextuality to identity, culture, literary theory and world literature. The recent reserch hotspots in this field are mainly identity, culture, world literature, literary theory, Latin American literature, appropriation, genre, theatre, ethics and digital humanities, etc.
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 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 | 0.010 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.203 | 0.296 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".