Bibliometric Analyses on Repository Contents for the Evaluation of Research at Wageningen UR
Bibliographic record
Abstract
Since the last two decennia, Wageningen UR Library has been involved in bibliometric analyses for the evaluation of scientific output of staff, chair groups and research institutes of Wageningen UR. In these advanced bibliometric analyses several indicator scores, such as the number of publications, number of citations and citation impacts, are calculated. For a fair comparison of scientific output from staff, chair groups or research institutes (that each work in a different scientific discipline with specific publication and citation habits) scores of the measured bibliometric indicators are normalized against average trend (or baseline) scores per research field. For the collection of scientific output that is subjected to the bibliometric analyses the repository Wageningen Yield (WaY) is used. This repository is filled from the research registration system Metis in which meta data for scientific output is registered by the secretaries of the research groups of Wageningen UR. By the application of a connection between the meta data of publications in WaY and citation scores in Thomson Reuters¿ Web of Science, custom-made analyses on the scientific output and citation impact of specific entities from Wageningen UR can be performed fast and efficiently. Moreover, a timely registration of new scientific output is stimulated (to ensure their inclusion in future bibliometric analyses) and the quality of meta data in WaY is checked by the library staff and by the research staff from the research entities under investigation, thus promoting communication between the library and customers
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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 | 0.038 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.150 | 0.196 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.008 |
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".