Let's Talk – Interoperability between University CRIS/IR and Researchfish: A Case Study from the UK
Bibliographic record
Abstract
Research funders and research organisations both require feedback on the progress, productivity and quality of the research they support. This information originates with researchers, but may be captured in a variety of systems including University CRIS/IR and funder systems. In 2014 all 7 national Research Councils (collectively referred to as RCUK) implemented a harmonised approach to the collection of research output data, currently supported by Researchfish Ltd (referred to as the Researchfish® system). In 2016 this process is gathering feedback from over 60,000 researchers in all UK Universities, and for funders in the USA, Canada, Denmark and the Netherlands, tracking more than £40billion of public and charity research investment and is adding to a dataset of more than 1.5 million outputs. Researchers, research managers and funders want to find ways to capture this data once and achieve wide re-use of the information. Working together University and Research Council officers, Researchfish Ltd. and Jisc have highlighted that it is important for the “interoperability” between research information systems to be improved. These organisations have started a programme of work to improve the bi-directional flow of information between University and funder systems.
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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 | 0.029 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.018 | 0.011 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".