Open Access Metrics: Use REF2014 to Validate Metrics for REF2020

Gopal T V gopal at ANNAUNIV.EDU
Fri Dec 19 16:11:05 EST 2014

Dear Dr. Steven Harnad,

I have been studying this "discussion thread".

I view the metrics [Parameters & Weightages are well appreciated] as a
means of positioning the research results globally with a "quality
indicator" leaving the facilitation to ensure the Identity & Vision of a
given Institution to its very own governance models.

Metrics do not automatically imply "knowing" both Science and the
Scientist [the generator of this Science] together for there is a
disconnect between the formula and the reality. If this is not assured, we
once again stare at the path towards the IPR.

IMHO, Metrics help or indicate correctives to "sluggishness" than a truly
means of observation or discovery.

As you would appreciate, Science has been expressed in a multitude of
languages and it may have to be that way as well.

A couple of quick questions:

How are translations of original works traced in "Open Access" ?

Is there a way of tracing the "mutations" in the progressive expressions
of research results ?

Your thoughts..

Warmest Regards

Gopal T V
0 98401 21302
Dr. T V Gopal
Department of Computer Science and Engineering
College of Engineering
Anna University
Chennai - 600 025, INDIA
Ph : (Off) 22351723 Extn. 3340
      (Res) 24454753
Home Page :

On Fri, December 19, 2014 10:03 pm, Stevan Harnad wrote:
> Adminstrative info for SIGMETRICS (for example unsubscribe):
> On Dec 19, 2014, at 5:06 AM, Jon Crowcroft <jon.crowcroft at>
>> I can see you might want 30 params to fit 7500 + Papers - what I am
saying is that the _noise_ will be dreadful and also there are
>> systematic reasons which I outlined that mean for low citation/low
>> count areas, you will have almost no fit at all
> 1. Actually, it’s not 7500 papers that are being fitted but the
> for about 154 institutions x 36 units of assessment (fields) = 5544.
> 2. You are right that 30 parameters is a lot for each unit of assessment
analyzed separately for its 154 rank data-points. Many of the weaker
metrics will probably have near zero weights, but the strong ones, and
> weight of their contributions, will be estimated.
> 3. For the low-paper, low-citation fields, the analysis will be
> likes with likes (and paper-counts and citation counts are not the only
potential metrics).
> I think much of the noise will be coming from the missing metrics
> of the missing OA.
>> the _obvious_ thing people will do is to move all their research to
areas which do have a good fit
> Another solution is to subdivide units of assessments more finely,
allowing low-count subfields to compete only among themselves, and then
recombine them giving each subfield an a-priori weight in the combined
total for the unit of assessment.
>> predictable research isn't (research)
> Agreed. (And I am not defending the REF per se, just trying to make the
best of it, by testing and validating metrics to supplement or replace
costly, time-consuming panel review.)
>> bad idea - sorry, I just fundamentally disagree about this approach…
> The REF, or the metric fitting of the peer rankings?
>> I don't dispute you can (over) fit the data….
> The objective is not just to fit the data, but to test how well the
metrics can predict the peer rankings, initialize their weights, and use
them to supplement or replace future peer rankings.
> (Of course their predictive power can also be tested by split-half
comparisons within the REF2014 sample; and of course the initial weights
can continue to be updated across time based on further peer rankings or
other criteria.)
>> On Thu, Dec 18, 2014 at 11:23 AM, Stevan Harnad <harnad at
<mailto:harnad at>> wrote:
>>> On Dec 18, 2014, at 3:39 AM,  [name deleted because posted off-list] 
>>> 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
>> 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
>> 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,
>> 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
>>>> 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
>>> 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
>>>> 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) +
>>> + 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
>>>>> (unheeded):
>>>>> Scientometric predictors of research performance need to be
>>>>> 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
>>>>> 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. 
>>>>> See also:
>>>>> The Only Substitute for Metrics is Better Metrics
>>>>> <>
>>>>> and
>>>>> On Metrics and Metaphysics
>>>>> <>

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