How should we judge scientific impact?

It’s a hard problem. For very applied work, it might be possible to see how the technology sector picks up various scientific advances and judge merit on that basis, but for most scientists the impact of their research is only seen on long timescales, much longer than the few years they spend on an individual project, or working with one particular group.

Traditionally, citations have been used as a measure of success. The assumption is that other scientists will be aiming to work on important topics, and so if they have cited some research it must have been useful to them, and therefore also important. So the more citations a paper has, the more useful it has been. The obvious problem with this analysis though is that it assumes that all citations contain the same amount of usefulness. In reality, there are many reasons to cite something beyond it being a genuinely useful inspiration to the research in question. Writers often cite well known works in their field, even if largely irrelevant to their current research, or cite a paper to explain that its interpretation of something or other is incorrect. And because citations are used as a measure of success, writers often cite their own papers, or those of their collaborators even when not really necessary.

This problem is most clearly uncovered in the work of Simkin & Roychowdhury’s 2002 paper, Read before you cite!  They reason that authors who read a paper, then decide to cite it have some small chance of making a typographical mistake when writing their bibliography (note, this was before the time of software reliably auto-generating bibliographies, as is commonplace now). So perhaps paper A is cited by paper B, but a spelling mistake is made in the citation. Paper C then cites both B, and A – but makes the exact same mistake as paper B did. Simkin & Roychowdhury then reason that it is likely that the authors of paper C didn’t read paper A at all – they just copied from the bibliography of paper B. After all, the chances of them just making an identical typo seem very small, and if you did read the paper you would presumably just write down the author’s names from the original. Using this simple model they calculate that around 80% of citations (in their dataset) are made without the cite even reading the paper they are citing!

This should all be very concerning for those of us who care about how many times our papers get cited. Measures such as the h-index, and impact factors are all ultimately based on counting citations. Does it mean that citation analysis is fundamentally flawed? I don’t think so – it just means that we need to use more sophisticated tools than just counting citations and look at other aspects of the topology of citation networks, and other information we have about papers. This has been the subject of some of my recent research, and so I hope to write a few more posts soon to ‘catch up’ to where that research currently is.

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