The four UK higher education funding bodies are consulting on proposals for the next Research Excellence Framework. Thank you to all Lis-Bibliometrics members who have contributed their thoughts on this. Here is a draft response the Lis-Bibliometrics Committee intends to submit on behalf of the group. If you have any last minute comments please contact me or share via the list as soon as possible. We’ve decided to respond only to consultation question 18:
Q.18 Do you agree with the proposal for using quantitative data to inform the assessment of outputs, where considered appropriate for the discipline? If you agree, have you any suggestions for data that could be provided to the panels at output and aggregate level?
We agree that quantitative data can support the assessment of outputs where considered appropriate by the discipline. Any use of quantitative data should follow the principles for responsible use of metrics set out in the Metric Tide and the Leiden Manifesto.
- Disciplinary difference, including citation patterns varying by output type, must be taken into account.
- Data should only be used if it offers a high standard of coverage, quality and transparency. Providing data from a range of sources (e.g. Scopus, Web of Science, Google Scholar) would allow the panel to benefit from the strengths of each source whilst highlighting the limitations.
- Known biases reflected by bibliometric indicators (e.g. around interdisciplinary research and gender) should be taken into account.
- A range of data should be provided to avoid incentivizing undesirable side effects or gaming by focusing attention on a single indicator.
- Given the skewed distribution of citations, and the ‘lumpiness’ of citations for recent publications in particular, we recommend measures of uncertainty be provided alongside any citation data. At the very least, false precision should be avoided.
- In addition to citation indicators, panels should take into account the number of authors of the output.
Panels should receive training on understanding and interpreting the data and be supported by an expert bibliometric advisor.
We do not consider the field-weighted citation impact indicator appropriate for the assessment of individual outputs: as an arithmetic mean based indicator it is too heavily skewed by small numbers of ‘unexpected’ citations. Furthermore its 4 year citation window would not capture the full citation impact of outputs from early in the REF period. The use of field-weighted citation percentiles (i.e. the percentile n such that the output is among the top n% most cited outputs worldwide for its subject area and year of publication) or percentile bands (as used in REF2014) is preferable. Percentile based indicators are more stable and easier to understand as the “performance” of papers is scaled from 1-100, but can be skewed by large numbers of uncited items.
Output level citation indicators are less useful for recent outputs. Consequently, it might be tempting to look at journal indicators. This temptation should be resisted! Given the wide distribution of citations to outputs within a journal, and issues of unscrupulous ‘gaming’, journal metrics are a poor proxy for individual output quality. Furthermore, use of journal metrics would incentivize the pursuit of a few ‘high impact’ journals to the detriment of timely, diverse and sustainable scholarly communications.
Use of aggregate level data raises the question of whether the analysis is performed only on the submitted outputs, or on the entire output from the institution during the census period. The latter would provide a more accurate picture of the institution’s performance within the discipline, but automatically mapping outputs to REF units of assessment is extremely challenging. Furthermore it would be hard to disaggregate those papers written by staff who are not eligible for submission to REF.
Katie Evans, on behalf of the Lis-Bibliometrics Committee
Note: This replaces an earlier draft REF consultation response posted on 1st March 2017.