Bibliographic record
Abstract
This paper first analyzed two studies on rater factors and rating criteria to raise the problem of rater agreement. After that the author reveals the causes of discrepencies in rating administration by discussing rater variability and rater bias. The author argues that rater bias can not be eliminated completely, we can only reduce the error to a cetain degree by training raters. The study on rater factors can help us better understand rater variability and rater bias, train raters more effectively and find out ways to modify the scores given by raters. The author suggests that rater files which contain rater information including each rater bias tendency should be established and kept so that information can be retrieved about the selection of raters, the interpretation as well as the modification of the scores given by raters. Raters need to receive pre-service training, on-service training and pilot -on-task training.
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 | 0.589 | 0.820 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier 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".