Research Progress and Trends of Pigai.org Automated Writing Evaluation System in English Writing: A Systematic Bibliometric Analysis (2011-2023)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.112 | 0.120 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it