Bibliographic record
Abstract
Artificial intelligence machine learning technology has become a hotspot for academic research. As one of the core topics of machine learning, clustering has penetrated into economic statistics, social media, biomedical and other fields. In order to quantitatively and visually measure and identify the development context and characteristics of cluster analysis, this paper uses Citespace to conduct in-depth analysis of publication quantity analysis, spatial analysis, keyword with the strongest citation analysis, etc. Trend of publication volume of cluster analysis shows a trend of first rising and then falling and the number of publications will peak in 2021. Publication distribution area whose main areas are America, China, and Italy, and Germany, Japan, Poland, Canada, and India have close cooperation. Keywords with the strongest citation bursts shows that" quality of life" has the highest mutation coefficient, and "outcome", "machine learning" have popped up recently. In conclusion, this paper develops an overview of clustering using bibliometrics, providing an innovative idea for research in the field of statistics.
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 | 0.004 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.071 | 0.119 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".