[GOAL] Fwd: [SIGMETRICS] Open Access Metrics: Use REF2014 to Validate Metrics for REF2020

[Stevan Harnad]

Re-posted to GOAL as of potential interest:

On Dec 18, 2014, at 10:57 AM, David Wojick <dwojick at CRAIGELLACHIE.US> wrote:

This does sound interesting, Stevan, especially if you got an unexpected
> result.

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
singularity!"*)

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 <http://www.ref.ac.uk> 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 recommend the right OA policy model to OSTI
<http://openaccess.eprints.org/index.php?serendipity%5Baction%5D=search&serendipity%5BsearchTerm%5D=wojick&serendipity%5BsearchButton%5D=%3E>
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:

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 <http://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 ecs.soton.ac.uk >
wrote:

 On Dec 17, 2014, at 9:54 AM, Alan Burns <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
<http://plato.stanford.edu/entries/induction-problem/>:

 " The man who is ready to prove that metaphysical knowledge is wholly
impossible… is a brother metaphysician with a rival theory
<https://www.goodreads.com/quotes/1369088-the-man-who-is-ready-to-prove-that-metaphysical-knowledge>â€
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
<https://www.goodreads.com/quotes/1369088-the-man-who-is-ready-to-prove-that-metaphysical-knowledge>
etrics…â€


 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…
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