The Scientific Articles on Art Criticism
Bibliographic record
Abstract
Research has been extremely involved in improving in the art criticism area. These improvements are reflected in scientific articles. This article purposed to investigate the 214 articles in art criticism to explore their main characteristics. These articles published in the Web of Science database of the Institute of Scientific Information (ISI) from the period of 1980 till 20 December 2013. Types of articles were article and review which is included in the study. The three top cited (more than 10 times citations) articles in art criticism were published in 1993 and 1999. The 214 articles mean citation rate was 0.87 (SD 2.38) times. Among the various fields, art (58.87%), arts humanities other topics (28.03%), both art and arts humanities other topics (5.14%), both art and education and educational research (2.33%), both art and history (1.40%), art, arts humanities other topics and literature (1.40%), both art and cultural studies (0.93%), both art and philosophy (0.93%), both art and literature (0.46%), and both arts humanities other topics and cultural studies (0.46%) were the most popular fields of research. The results showed that researches were done in the United States had highest citation which was written in English language.
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 | 0.014 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.040 | 0.061 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".