{"id":65,"date":"2025-12-07T14:20:09","date_gmt":"2025-12-07T14:20:09","guid":{"rendered":"https:\/\/alandix.com\/ref2021\/?page_id=65"},"modified":"2025-12-07T14:22:32","modified_gmt":"2025-12-07T14:22:32","slug":"5-discussion","status":"publish","type":"page","link":"https:\/\/alandix.com\/ref2021\/report\/5-discussion\/","title":{"rendered":"5.\u00a0 Discussion"},"content":{"rendered":"

5.1\u00a0 Factors that might affect the accuracy or trends of results<\/h2>\n

As noted there may have been implicit bias affecting REF results, but based on the 2014 outcomes if anything this would have acted to make the value for money more extreme.<\/p>\n

There might also be differences across different areas of computing.\u00a0 While there are few areas requiring large-scale equipment as there are in Physics, theoretical computing may require less resources than more application-oriented areas.\u00a0 However, within computing the post-1992 institutions are more likely to have application-focused research, so again this would mean if anything that the underlying trend is even more extreme than is apparent in the REF data.<\/p>\n

Academics have multiple demands on their time conflicting with research.\u00a0 However, one would expect these time demands to be greater in teaching-intensive institutions, so one might therefore expect less efficient use of research time, but that is the opposite of the trend seen.\u00a0 However, competing workloads may well be a factor at the level of individual institutions.<\/p>\n

Similarly, one would expect the research environment in research-heavy institutions to be more conducive to effective use of research resources, but this is the opposite of what is seen in the data.<\/p>\n

5.2\u00a0 Alternative analyses<\/h2>\n

Funding comes for various purposes, some is focused on pure research (esp. UKRI), some for industrial projects, some for work with communities or local business, some for doctoral training, etc.\u00a0 The different value-for-money measures all use the overall<\/em> income figure and do not attempt to model the individual types of income and how they map to the three main REF criteria areas.\u00a0 Given there are only around ninety institutions and many factors, a more complex analysis would struggle to avoid overfitting, but this could perhaps be attempted by pooling data from several UoAs with similar characteristics.<\/p>\n

5.3\u00a0 Individual institution results vs overall trends<\/h2>\n

I was Director of the Computational Foundry at Swansea University over a substantial part of the REF period, so it is somewhat embarrassing that Swansea sits low in the income\u2013GPA curve in Figure 10.\u00a0 However, I am also aware that during the period the Computational Foundry hosted Cherish DE a large network-style grant that registered as Swansea income but had widespread benefits across UK HE; in addition, student numbers grew far faster than staff during the REF period leading to one of the worst computing SSRs in the UK.\u00a0 Without making excuses (!), there will be similar stories across every institution and so the data in these \u2018league tables\u2019 should not be read as producing an accurate value-for-money estimate for each institution in UoA11.\u00a0 However, the overall trends across sectors of UK HE are likely to be robust.<\/p>\n

\"\"<\/a><\/p>\n

Fig 10. Graph of overall GPA vs Total income for academic years 2013-14 to 2019-20
\n(Swansea University highlighted)<\/p>\n

5.4\u00a0 Possible reasons for the trends seen<\/h2>\n

Assuming these results are robust, why are we seeing the overall trend to greater effectiveness in lower-ranked institutions.\u00a0 This is an important question as understanding this might allow improved research effectiveness everywhere.<\/p>\n

Here are some possible reasons for the differing value for money across the sector, which may all apply to some extent.\u00a0 Some are about the overall funding system:<\/p>\n