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We've already discussed about which bibliometric indeces are diffused the most, and how they work.

We all know that, along the most common h-index, there are many other parameters deriving from it or similar to it. From Publish or Perish website, we can list:

Between these (and other) bibliometrics indeces, which one do you trust more? Which one do you think is the best one to get the scientific excellence of an author? Why?

Thanks!

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    a more succinct answer to your question is Goodhart's law: en.wikipedia.org/wiki/Goodhart%27s_law – Suresh Jul 18 '12 at 15:50
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    If we are to reduce someone's scholarly worth to a number, allow me to (facetiously) suggest the Erdős–Bacon number: tinyurl.com/ylqt2h – Ben Norris Jul 19 '12 at 0:58
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    "Not everything that counts can be counted, and not everything that can be counted counts." — William Bruce Cameron (sometimes misattributed to Albert Einstein) – JeffE Jul 19 '12 at 5:40
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The short answer is NONE. A longer answer is "to get the scientific excellence of an author, read their papers and understand their contributions".

The problem here is in expecting a number to characterize the contributions and quality of an individual researcher. Probably the only way in which these measures can be useful (and that's stretching it a LOT) is if they are viewed in aggregate (for a department/university) to get a very crude picture of research productivity (not excellence).

But the signal is so noisy as to be useless.

Really, there's no shortcut for the hard work of reading, researching and asking in order to assess the "scientific excellence" of a researcher.

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    I really wouldn't :). It's like asking me to choose which knife to cut myself with. – Suresh Jul 18 '12 at 15:33
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    To give a somewhat inflammatory analogy, it's like being asked whether, if one were forced to choose one or the other, it's better to judge researchers based on race or sex. There's just no good answer. – Anonymous Mathematician Jul 18 '12 at 15:39
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    (+1) To add to this, all of the different proposed metrics are simply some type of numerical transformation based upon (essentially) the same information. Unless the information with which to base the score off improves to be a better/reasonable indicator of "scientific excellence", no amount of transformation to those metrics will make them a better indicator. The saying "garbage in - garbage out" is apropos here. – Andy W Jul 18 '12 at 15:43
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    Good point @AndyW: in fact the American Statistics Association did an analysis a while back showing that h-index correlates strongly with the square root of total citations. – Suresh Jul 18 '12 at 15:48
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    There is some discussion of this here: michaelnielsen.org/blog/why-the-h-index-is-virtually-no-use – Aaron Jul 19 '12 at 20:40
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You can trust them all, equally. Each index has a precise meaning that is well defined (once the database used is given). For example, if a researcher has h-index 3, you know that she has at least 3 papers with at least 3 citation each, and that she has not 4 papers with more than 3 citation each, and this is quite reliable informations.

Now, what you probably mean is whether we can trust some index to say something else; but what? Research productivity? Academic success?

It's just like the number of books sold by a novelist. You can trust it to tell you how many books she sold, and it may help you decide whether you want to publish its next one if you are only interested in sales. Of course if you want to decide whether she deserves the Nobel prize, you might not want to base your decision on this index.

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Indices are (unfortunately) liked by managers, administrators and funding agencies rather than by researchers. Hence, you should ask these managers, administrators and agencies which indices they trust (and why). I am not sure that academia.SE is the right place to find managers...

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