Bibliographic record
Abstract
The study analyses the mapping of the research publications on research ethics, the Scientometrics profile was applied in the field of research during 2000-2022. The data out of 78308 publications, the largest 8751 (10.71%) of publications was distributed by the researchers in 2021. The EGR ranges between 0.068 and 2.044 in the years 2002 and 2019 respectively. The study found ranked authors, out of which twenty-five authors in the research field were concerned. The most prolific author found in the series, Shine, was the top ranked and had published 164 (0.201%) of the publications. The study explored the productivity of the publications contributed among twenty-five researchers. The ranking of the counties in order was England in second with 13353 (16.34%), then Australia 11777 (14.41%), Canada 8424 (10.31%), Peoples Republic of China 5293 (6.48%), Germany 4512 (5.52%), Netherlands 3920 (4.79%), France 2733 (3.34%), Spain 2391 (2.92%) and Sweden 2294 (2.80%).
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 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 | 0.050 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.008 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".