Research Progress and Trends of Pigai.org Automated Writing Evaluation System in English Writing: A Systematic Bibliometric Analysis (2011-2023)
Bibliographic record
Abstract
This study presents a systematic literature review on the application of the Pigai.org automated writing evaluation system in English writing in China. It aims to explore the research progress and trends in English writing during the period (2011-2023). Journal papers in this study were extracted from the two main databases: CNKI and EBSCOhost, dating from January 1, 2011, to June 22, 2023. The PRISMA 2020 guidelines and the bibliometric analysis technique design were adopted to conduct an in-depth analysis of the journal articles on the Pigai.org automatic writing evaluation system. Additionally, a comparative study on Performance Analysis, Science Mapping and Network Analysis on the number of published articles, authors, institutions, h-index(h) papers, and keywords were identified. Besides that, the thematic analysis of the keywords revealed the research focuses of Pigai.org in English writing education. Thus, the findings culminated the research progress and trends in China and the world by discussing the implications of the results and indicated promising directions for future research.
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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 | 0.054 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.065 | 0.059 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".