Warren Smart has analysed the recent performance of Australian and NZ universities in the three most prominent international university rankings (ARWU, QS, THE). There is a lot of detail there, not all of it depressing. It is going to be hard for NZ to keep being satisfied with “punching above its weight” in the face of lower income per student than just about anywhere we want to compare ourselves with. As a country, we may indeed have too many universities for them all to rank well internationally. But the good thing about NZ is that change can happen rapidly. So, please consider the policies of all parties in the areas of tertiary education, research, innovation, etc when voting in the general election on 20 September 2014.
I was invited this year to KiwiFoo camp run 11-13 April by Nat Torkington and his crew in Warkworth. Before I went, I expected from reading others’ accounts of past camps that it would be (over?)stimulating and not to be missed, and so it proved to be. The opportunity to mingle with and listen to a diverse group of around 200 intelligent and articulate people (mostly with a common belief that technology can make the world better) doesn’t come along often. Certainly it is the first time I have seen journalists, bureaucrats, politicians, scientists, entrepreneurs, programmers and teachers thrown together in this way. Although there is always the danger of sessions degenerating into discussions about society that generalize from the experience of the participants (who are certainly not representative of NZ society) without sufficient data, this must be how major changes in society start. I hope that many good actions are inspired by our discussions.
If you ever get an invitation to KiwiFoo, accept it!
Sometimes it is easy to forget that there may still be people who are not informed about this issue.
- A nice summary by Samuel Gershman. He doesn’t mention one reason for the status quo being so hard to change: each journal has a monopoly on papers, and publisher packages (“the Big Deal”) make it hard to cancel individual journals – in any case, authors are insulated from having to make decisions about publication venues based on price.
- Peter Murray-Rust is rightly angry about devious/incompetent publishers getting in the way
- A great title: Causes for the persistence of impact factor mania
- Meanwhile, Elsevier (anagram of Evil Seer) just keeps on going, with rising profits
- The University of Waikato now has an open access “mandate” (a bit toothless for that name, really a policy, but a reasonable start). I have seen claims that it is the first in NZ, but it seems Lincoln University beat them to that. I know the University of Auckland has a working group on this issue. So, some slow progress, and maybe in my lifetime we will get where we ought to be already.
From Head of Department:
Just to let know that the three new lecturer positions in our department are now live on the university vacancies page. Closing date of 31 March for applications, so please pass the news around top candidates that you are feel might be interested. The three positions are:
As I submit yet another low-probability grant bid that took up too much of my time, once again thoughts that “there must be a better way” come to mind. It seems that many colleagues feel the same way. Some interesting reading:
- Is grant writing taking over science?
- Australia’s grant system wastes time (paywalled)
- Cost of the NSERC Science Grant Peer Review System exceeds the cost of giving every qualified researcher a baseline grant
- Modelling academic research funding as a resource allocation problem
- Big Science vs. Little Science: How Scientific Impact Scales with Funding
I have always felt that adding a random component to the grant award system, so as not to waste so much time trying to distinguish between very similar proposals, and giving out more, but smaller, grants, would be improvements. Reading comments on the above articles shows that several others agree. Perhaps it is time to try out some more modelling!
I just attended this concert. It was one of the most enjoyable, and moving in many parts, theatre experiences I have had. If you have a chance to see them, take it.
Open Access Week was low-key at UoA this year. I couldn’t attend a talk by Matt McGregor from Creative Commons Aoteoroa – here are the slides. I was on a panel discussion concerning measures of research impact, which included Jason Priem of ImpactStory and Andrew Preston of Publons.Thanks to Fabiana Kubke and Siouxsie Wiles for organizing.
In the last few years I have often read about the crisis in scholarly (mostly scientific) peer review.
I share the belief that the current system is surely suboptimal and must be changed. Much of what I say below is not original: I have read so many posts and books by Tim Gowers, Bjoern Brembs, Mike Taylor, Michael Nielsen, Michael Eisen, Noam Nisan, and many other people, that I can’t remember them all. I have a year’s experience as managing editor of an open access no-fee journal, two years’ experience as an editor (= referee) for a Hindawi journal, and many years experience as a journal referee. My research area is mathematics and various applications, so there may be some discipline-specific assumptions that don’t work for other fields. And it is not possible to cover every issue in a blog post, so I don’t claim to be comprehensive.
The latest online furore was occasioned by a so-called “sting” operation published in Science (unlike most articles in that magazine, this one is freely readable). I don’t think it worth commenting in detail on that specific article. It tells us little we didn’t know already, and missed a big opportunity to do a more comprehensive study. It does show by example that pre-publication peer review can fail spectacularly. Some other (often amusing) instances from the last few years involve computer-generated papers that are much low quality than the one submitted by Science, presumably accepted by computer-generated editors (even mathematics is not immune and some journals have done this more than once).
Some people have claimed that these weaknesses in peer review are exacerbated by the pay-to-publish model (they are certainly not exclusive to such journals, as the examples above, some published by Elsevier in toll access journals, show). This model certainly does lead to clear incentives for “Gold OA” journals to publish very weak papers. However, since authors have to pay, there are countervailing incentives for authors. If the reward system is poorly organized (as it seems to be in China, for example), then authors may still choose these predatory journals. But since papers in them are unlikely to be read or cited much, it seems unlikely to create a large problem. Journal reputation counts too – most predatory journals receive few submissions, for good reason. The existence of many low quality outlets (which predates the rise of open access journals) is a nuisance and possible trap for inexperienced researchers, and reduces the signal/noise ratio, but is not the main problem.
The main problem is: the currently dominant model of pre-publication peer review by a small number of people who don’t receive any proper payment for their time, either in money or reputation, is unlikely to achieve the desired quality control, no matter how expert these reviewers are. Furthermore, our post-publication system of review to ensure reliability is rudimentary, and corrections and retractions are not well integrated into the literature.
Both deliberate fraud (still quite rare, given the reputational risks, but apparently much more common than I would have thought) and works that are “not even wrong” and thus can’t be checked (poorly designed experiments, mathematical gibberish, etc) slip through far too often. It is bad enough that there are too many interesting papers to read, and then a lot of solid but uninteresting ones. Having to waste time with, or be fooled by, papers that are unreliable is inefficient for readers, and allowing this to go on creates wrong incentives for unscrupulous authors.
It seems now that “publication” doesn’t mean much, since the barrier is so low. A research paper now has no more status than a seminar talk (perhaps less in many cases). Self-publication on the internet is simple. There are so many journals that almost anything can be published eventually. How can we find the interesting and reliable research?
- adopt the open research model
This means more than just making the polished research article freely available. It includes circulation of preliminary results and data. Certainly a paper that doesn’t allow readers to make their own conclusions from the data should be considered anecdotal and not even wrong. Imagine a mathematics paper that doesn’t give any proofs.
- decouple “peer review” from publication
There can be two kinds of services: assistance (writing tips, pointers to literature, spotting errors) with the paper before it is ready for “publication”, and comment and rating services (which can give more refined grades on quality, not just the current yes/no score.)
Journal peer review focuses on the second type, but only gives yes/no scores (sometimes, a recommendation to submit to another journal). Computer science conferences are good for the first type of review, in my experience, but bad at the second. The first type of service was is offered by Rubriq, Peerage of Science, Science Open Reviewed. The second type is currently offered by Publons, SelectedPapers.net (no ratings yet), PubPeer.
This allows people with more time to specialize in reviewing, rather than writing. And they should get credit for it! A colleague in our mathematics department told me in June that he had just received his 40th referee request for the year. He is too busy writing good papers to do anything like that amount of work. Yet PhD students and postdocs, or retired researchers, or those with good training whose job description does include intensive research (such as teaching colleges) could do this job well. To keep this post from getting even longer, I will not discuss anonymity in reviewing, but it is an important topic.
Other advantages are that post-publication review boards could bid for papers, so the best ones are reviewed quickly by the best reviewers, multiple review boards could review papers, and reviews are not wasted by being hidden in a particular journal’s editorial process.
- decouple “significance” from inherent quality measures
Journals also routinely reject on grounds of their own idea of “significance”, which is inefficient (especially when they publish “important” work that is “not even wrong”). The real determination of how important and interesting a paper is can only be done after publication and takes a long time. In some fields, replication must be attempted before importance can be determined. PLoS does this kind of filtering and seems to be successful. Pre-registration of experimental trials which will lead to publication whatever the result, and registered replication reports, are other ways to reduce the bias toward “glamour mag science”.
- if you want attention for your work, you may have to pay for it
There ought to be a barrier to consuming expert time. It is limited, and refereeing junk papers for free is a big waste of it. I would like to see a situation where it costs authors something (money, reputation points, in-kind work) to command attention from someone else (if the work is exciting enough that people will do it for free, then so much the better). This doesn’t preclude authors making drafts available and seeking freely given feedback. However, more detailed pre-publication attention might be obtained by various means: give seminar talks and present at conferences, pay via money or formalized reciprocal arrangement. Post-publication attention is another matter.
- complete the feedback loop
No system can work well unless information on performance and opinions is allowed to flow freely. Reviewers must themselves be able to be reviewed and compared. Strong ethical guidelines for reviewers should be set, and enforced. The current system allows anonymous referees to do a poor job or an excellent one, and only their editor knows both who they are and their performance level.
Connected Researchers gives a good list of “Science 2.0″ tools, which should be useful.
The New Zealand Labour party will soon have an election for leader of its Parliamentary caucus. The voting system is a weighted form of instant runoff using the single seat version of Hare’s method (instant runoff/IRV/alternative vote). IRV works as follows. Each voter submits a full preference order of the candidates (I am not sure what happens if a voter doesn’t rank all candidates but presumably the method can still work). In each round, the voter with smallest number of first preferences (the plurality loser) is eliminated, and the candidate removed from the preference orders, keeping the order of the other candidates the same. If there is a tie for the plurality loser in a round, this must be broken somehow.
The NZLP variant differs from the above only in that not all voters have the same weight. In fact, the caucus (34 members) has a total weight of 40%, the party members (tens of thousands, presumably) have total weight 40%, and the 6 affiliated trade unions have total weight 20%, the weight being proportional to their size. It is not completely clear to me how the unions vote, but it seems that most of them will give all their weight to a single preference order, decided by union leaders with some level of consultation with members. Thus in effect there are 34 voters each with weight 20/17, 6 with total weight 20, and the rest of the weight (total 40) is distributed equally among tens of thousands of voters. Note that the total weight of the unions is half the total weight of the caucus, which equals the total weight of the individual members.
IRV is known to be susceptible to several paradoxes. Of course essentially all voting rules are, but the particular ones for IRV include the participation paradoxes which have always seemed to me to be particularly bad. It is possible, for example, for a candidate to win when some of his supporters fail to vote, but lose when they come out to vote for him, without any change in other voters’ behaviour (Positive Participation Paradox). This can’t happen with three candidates, which is the situation we are interested in (we denote the candidate C, J, R). But the Negative Participation Paradox can occur: a losing candidate becomes a winner when new voters ranking him last turn out to vote.
The particular election is interesting because there is no clear front-runner and the three groups of voters apparently have quite different opinions. Recent polling suggests that the unions mostly will vote CJR. In the caucus, more than half have R as first choice, and many apparently have C as last. Less information is available about the party members but it seems likely that C has most first preferences, followed by J and R.
The following scenario on preference orders is consistent with this data: RCJ 25%, RJC 7%, CRJ 10%, CJR 30%, JRC 20%, JCR 8%. In this case, J is eliminated in the first round and R wins over C in the final round by 52% to 48%. Suppose now that instead of abstaining, enough previously unmotivated voters decide to vote JRC (perhaps because of positive media coverage for J and a deep dislike of C). Here “enough” means “more than 4% of the total turnout before they changed their minds, but not more than 30%”. Then R is eliminated in the first round, and C wins easily over J. So by trying to support J and signal displeasure with C, these extra voters help to achieve a worse outcome than if they had stayed at home.
The result of the election will be announced within a week, and I may perhaps say more then.