Open Access Metrics: Use REF2014 to Validate Metrics for REF2020

Stevan Harnad harnad at ECS.SOTON.AC.UK
Thu Dec 18 06:23:25 EST 2014

> On Dec 18, 2014, at 3:39 AM,  [name deleted because posted off-list]  wrote:
> that's very high dimensionality in that equation.

I don’t think 30 metric predictors for about 6% of the planet’s annual research output  (UK) is such an under-fit.

(But we could start with the most likely metrics first, and then see how much variance is accounted for by adding more.)
> you don't have enough data points to have any decent confidence about those weights - i

That cannot be stated in advance. First we need to calculate the multiple regression on the REF2014 rankings and determine how much each metric contributes.

> suggest you look at the REF data… and see how many different journal/venues and all over the ACM Classification hierarchy, the 7000 odd outputs appeared in - you'll find in any given venue, topic you rarely have more than a handful of items - your variance will be terrible

The proposal is not to assess the predictive power of any one of the 4 publications submitted. 

The REF2014 peer rankings themselves are based on peers (putatively) re-reading those 4 pubs per researcher, but the regression equation I sketched is based on (OA) data that go far beyond that. 

(In point of fact, it’s absurd and arbitrary to base the REF assessment on just 4 papers in a 6-year stretch. That restriction is dictated by the demands of the peers having to read all those papers, but open-access metrics can be harvested and have no such human bottleneck constraint on them. What you could complain, legitimately, is that not all those potential data are OA yet... Well, yes — and that’s part of the point.)

REF2020Rank = 

w1(pubcount) + w2(JIF) + w3(cites) +w4(art-age) + w5(art-growth) + w6(hits) + w7(cite-peak-latency) + w8(hit-peak-latency) + w9(citedecay) + w10(hitdecay) + w11(hub-score) + w12(authority+score) + w13(h-index) + w14(prior-funding) +w15(bookcites) + w16(student-counts) + w17(co-cites + w18(co-hits) + w19(co-authors) + w20(endogamy) + w21(exogamy) + w22(co-text) + w23(tweets) + w24(tags), + w25(comments) + w26(acad-likes) etc. etc.

> and the result of munging all those _different_ distributions into one single model will be to prssure people to move their work areas to the best fit topic/venue, which is not a true measure of anything desired by us of HEFCE or RC.UK <> to my knowledge.

I cannot fathom what one, two, three or N things a researcher can do in order to maximize their score on the above equation (other than to try to do good, important, useful work…).

> please do the detailed work…

Will try. But there a few details you need to get straight too… (<:3

> On Wed, Dec 17, 2014 at 3:38 PM, Stevan Harnad <harnad at <mailto:harnad at>> wrote:
>> On Dec 17, 2014, at 9:54 AM, Alan Burns <alan.burns at YORK.AC.UK <mailto:alan.burns at YORK.AC.UK>> wrote:
>> Those that advocate metrics have never, to at least my satisfaction, answered the
>> argument that accuracy in the past does not mean effectiveness in the future,
>> once the game has changed.
> I recommend Bradley on metaphysics and Hume on induction <>:
> "The man who is ready to prove that metaphysical knowledge is wholly impossible… is a brother metaphysician with a rival theory <>” Bradley, F. H. (1893) Appearance and Reality
> One could have asked the same question about apples continuing to fall down in future, rather than up.
> Yes, single metrics can be abused, but not only van abuses be named and shamed when detected, but it become harder to abuse metrics when they are part of a multiple, inter-correlated vector, with disciplinary profiles on their normal interactions: someone dispatching a robot to download his papers would quickly be caught out when the usual correlation between downloads and later citations fails to appear. Add more variables and it gets even harder,
>> Even if one was able to define a set of metrics that perfectly matches REF2014.
>> The announcement that these metric would be used in REF2020 would
>> immediately invalidate there use.
> In a weighted vector of multiple metrics like the sample I had listed, it’s no use to a researcher if told that for REF2020 the mertic equation will be the following, with the following weights for their particular discipline:
> w1(pubcount) + w2(JIF) + w3(cites) +w4(art-age) + w5(art-growth)  w6(hits) +w7(cite-peak-latency) + w8(hit-peak-latency) +w9(citedecay) +w10(hitdecay) + w11(hub-score) + w12(authority+score) + w13(h-index) + w14(prior-funding) +w15(bookcites) + w16(student-counts) + w17(co-cites + w18(co-hits) + w19(co-authors) + w20(endogamy) + w21(exogamy) + w22(co-text) + w23(tweets) + w24(tags), +w25(comments) + w26(acad-likes) etc. etc.
> The potential list could be much longer, and the weights can be positive or negative, and varying by discipline.
> "The man who is ready to prove that metric knowledge is wholly impossible… is a brother metrician with rival m <>etrics…”
>> if you wanted to do this properly, you should have to take a lot of outputs that were NOT submitted and run any metric scheme on them as well as those submitted. 
>>> too late:)

You would indeed — and that’s why it all has to be made OA…

>>> On Wed, Dec 17, 2014 at 2:26 PM, Stevan Harnad <harnad at <mailto:harnad at>> wrote:
>>> Steven Hill of HEFCE has posted “an overview of the work HEFCE are currently commissioning which they are hoping will build a robust evidence base for research assessment” in LSE Impact Blog 12(17) 2014 entitled Time for REFlection: HEFCE look ahead to provide rounded evaluation of the REF <>
>>> Let me add a suggestion, updated for REF2014, that I have made before (unheeded):
>>> Scientometric predictors of research performance need to be validated by showing that they have a high correlation with the external criterion they are trying to predict. The UK Research Excellence Framework (REF) -- together with the growing movement toward making the full-texts of research articles freely available on the web -- offer a unique opportunity to test and validate a wealth of old and new scientometric predictors, through multiple regression analysis: Publications, journal impact factors, citations, co-citations, citation chronometrics (age, growth, latency to peak, decay rate), hub/authority scores, h-index, prior funding, student counts, co-authorship scores, endogamy/exogamy, textual proximity, download/co-downloads and their chronometrics, tweets, tags, etc.) can all be tested and validated jointly, discipline by discipline, against their REF panel rankings in REF2014. The weights of each predictor can be calibrated to maximize the joint correlation with the rankings. Open Access Scientometrics will provide powerful new means of navigating, evaluating, predicting and analyzing the growing Open Access database, as well as powerful incentives for making it grow faster.
>>> Harnad, S. (2009) Open Access Scientometrics and the UK Research Assessment Exercise <>. Scientometrics 79 (1) Also in Proceedings of 11th Annual Meeting of the International Society for Scientometrics and Informetrics 11(1), pp. 27-33, Madrid, Spain. Torres-Salinas, D. and Moed, H. F., Eds.  (2007) 
>>> See also:
>>> The Only Substitute for Metrics is Better Metrics <> (2014)
>>> and
>>> On Metrics and Metaphysics <> (2008)

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