Open Access Metrics: Use REF2014 to Validate Metrics for REF2020

David Wojick dwojick at CRAIGELLACHIE.US
Thu Dec 18 12:58:11 EST 2014

Stevan, Regarding the organization, I thought you were trying to match the 
REF rankings. Those were produced by a single organization, using a 
specific decision process, not by all the researchers and universities 
whose work was submitted. Also, the credibility I am referring to is that 
of the analysis, not of the metrics you choose to use. You seem to be 
giving this analysis more credence than it probably deserves. As i said, 
multiple regression analysis is a crude approach to decision modeling.

I do not see what any of this has to do with OA policy, especially US 
policy, just because you want to do some computations based on the REF 
results. And it sounds like you cannot do them because the metrical data is 
not available. It is a possibly interesting experiment, but that is all as 
far as I can see, not a reason to make or change policies.


At 12:39 PM 12/18/2014, you wrote:
>Adminstrative info for SIGMETRICS (for example unsubscribe): 
> On Dec 18, 2014, at 10:57 AM, 
>David Wojick <<mailto:dwojick at CRAIGELLACHIE.US>dwojick at CRAIGELLACHIE.US> wrote:
>>This does sound interesting, Stevan, especially if you got an unexpected 
>The objective is actually not to get an unexpected result, David, but to 
>generate a battery of metrics that predicts the actual REF2014 peer 
>ranking as closely as possible, so that in REF2020 it can be the metrics 
>rather than the peers that do the ranking.
>>But I doubt it would validate or invalidate any scientometric predictors.
>A high correlation would certainly validate the REF battery, for the REF.
>>It is basically a decision model for a single organization going through 
>>a more or less single, albeit complex, decision exercise. To begin with, 
>>it is just one organization.
>All researchers, at all UK institutions, in each discipline, is a 
>“single organization”?
>(To paraphrase an erstwhile UK researcher: "some organization!" "some 
>The UK does 6-11% of the world’s research. Not a bad sample, I’d say, 
>for a first pass at validating those metrics.
>>Then too, simple multiple regression seems like a very crude way to 
>>derive such a model.
>Simple multiple regression is a natural first step. (I agree that after 
>that more sophisticated analyses will be possible too.)
>>The large number of factors is also a concern, as others have noted, 
>>especially if we are trying to establish causality.
>For the REF, all you need is predictivity. But I agree that causality too 
>is important, and with continuous assessment instead of just stratified 
>post-hoc sampling, it will be possible to make much more powerful use of 
>the time domain.
>(I don’t think a starting battery of 30 metrics would be too many -- far 
>from it. But some of them will prove to have low or no Beta weights. 
>That’s why metric validation is an empirical exercise.)
>>I would think that the more factors used the less credible the result.
>The credibility of each metric will be the proportion of the total 
>variance that it accounts for. It is an empirical question whether a few 
>metrics will account for the lion’s share of the variance, and the rest 
>will have negligibly small or no weights.
>>But then we also need to think that we have all the significant factors, 
>>don't we? Perhaps not. Are there useful precedents for this?
>I am certain that my back-of-the-matchbox list of candidate metrics was 
>neither exhaustive nor optimal. It was just indicative. All other credible 
>candidates are welcome!
>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.
>>Finally, is all the needed data available and how much might this cost?
>The <>REF2014 data were released today and are 
>available at once, for testing against metrics, discipline by discipline.
>What’s still very sparse and gappy is the availability of the 26 OA 
>metrics sketched above — and that’s because a lot of the source material 
>is not yet OA. The proprietary databases (like WoS and SCOPUS) are not OA 
>either. But if the papers were all OA, then the metrics could all easily 
>be harvested and calculated from them.
>>I guess that if I were peer reviewing this as a preliminary proposal I 
>>would be positive but not enthusiastic. More information is needed about 
>>the proposed project and its goals.
>I wasn’t actually counting on your recommendation for peer review of the 
>proposal to validate metrics against REF2014, David: I was rather hoping 
>it might help inspire you to 
>the right OA policy model to OSTI  for which you consult. That way we 
>would have a better hope of making the all-important OA data available 
>when President Obama’s OSTP directive is implemented...
>>At 07:23 AM 12/18/2014, you wrote:
>>>Adminstrative info for SIGMETRICS (for example unsubscribe): 
>>>>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 dataÂ
 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 workÂ
>>>>please do the detailed workÂ

>>>Will try. But there a few details you need to get straight tooÂ
>>>>On Wed, Dec 17, 2014 at 3:38 PM, Stevan Harnad 
>>>><<mailto:harnad at>harnad at > wrote:
>>>>>On Dec 17, 2014, at 9:54 AM, Alan Burns 
>>>>><<mailto:alan.burns at YORK.AC.UK>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 
>>>>>once the game has changed.
>>>>I recommend Bradley on metaphysics and Hume on 
>>>>The man who is ready to prove that metaphysical knowledge is wholly 
 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 
>>>>>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 
 is a brother metrician with rival metricsÂ
>>>>>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 OAÂ

>>>>>>On Wed, Dec 17, 2014 at 2:26 PM, Stevan Harnad 
>>>>>><<mailto:harnad at>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 
>>>>>>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 
>>>>>>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:
>>>>>>Only Substitute for Metrics is Better Metrics (2014)
>>>>>>Metrics and Metaphysics (2008)
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