While we enjoyed reading the recent articles by Slater (“Strings and Things: A Unificational Meta-
Of course, given Slater’s general lack of energy for his work (see fn. 3), we suggest he either take up a less demanding academic pursuit
Our model of linguistic fame is significantly more nuanced, much better researched, supported by remarkably more evidence, appreciably more elegant, markedly more explanatory, and
So far, the “literature” on this subject has produced two formulæ, in which ΔT represents a change in fame accrued to a theory or theoretician, ΔΘ and ΔE represent measures of the amount of data involved, Ω represents real-
ΔT = ΔΘ-Ω
(ΔT) (ΔE) ≥ ћ
Both attempt to capture what’s really going on here, but they miss several key ingredients that contribute to a more nuanced understanding of linguistic fame:
FA: The current level of fame of the most famous author on a paper. Fame accrues in a Zipfian manner, with the most famous garnering more fame merely for having spoken, regardless of other merits. You can estimate the fame of an author by applying the final fame formula iteratively to all of the author’s publications over their career.
N: The number of authors on a paper. More sharing leads to less fame; in particular, the key relation is to the inverse of √N. Lone genius is the best approach, but one co-author won’t drag you down too much, especially if that co-author is significantly more famous than you. (Specifically, √2 ≈ 1.41421 times as famous as you.) Having many co-authors should thus be avoided.
IF: The impact factor of the journal in which the paper is published. More impactful fame is more impactful.
δJ: The distance between the field of the journal in which the paper is published and the field of linguistics. Any linguist can publish in a linguistics journal, but only an überlinguist can publish a linguistics paper in a biology journal. The number of steps required to traverse from linguistics to the discipline in question in any reasonable academic ontology will do as a distance metric here.
sC·i: The “sciencey” factor of a journal field. These factors have to be determined empirically, so some sample values are provided here. Linguistics has been normalized to 1.
∞ Mathematics 137.035 Physics 42.0 Chemistry 41.9 Computer Science 6.79 Biology 1.14 Psychology 1.01 Law 1.00 Linguistics
0.74 Anthropology 0.51 Sociology 0.43 History 0.39 Philosophy 0.23 Music 0.12 Theater 0.0001 Literary studies 0.00 Post Modern Literary Criticism
χg: The xerox-
τf: The trendiness, at the time of publication, of the theoretical framework in which the theory is couched. Shiny new frameworks always garner more attention than older, better understood frameworks in which refutations are more easily formulated. Measured in the standard onomastic unit of Kaytlynns.
β⦿g: The bogosity of the theory. The actual theoretical worth of the theory has some impact on its fame. The effect is small, but measurable. Bogosity is measured in the standard A.I. unit of µLenats.
Θ: The amount of data used in the publication (called E by Colden). There are actually two relevant measures, Θ, the number of morphemes in distinct glossed examples, and ΔΘ, the number of morphemes in new distinct glossed examples. New data is much more dangerous to a theory and thus to fame, because it is subject to new interpretation and discussion by those who would seek to refute you; also, no one really re-reads old examples.
ћ: The number of speakers of the language of the example data provided. Also to prevent division-
ε: Slater and Colden both fail to take into account that Θ can be zero. While it takes an exceptionally bold linguist and a slightly drunk editor for it to happen, such data-
Ω: The real-
73.9 Can be used by fieldworkers while taking field notes 11.8 Can be used to write a reference grammar 3.47 Can be used to do graduate homework problems 2.16 Can be used to do undergrad homework problems 1.55 Can be explained in a textbook aimed at undergrads 1.04 Can be explained in a textbook aimed at grad students 0.53 Cannot be understood by professional linguists who do not have a background in statistics 0.32 Cannot be understood by professional linguists who do not have a background in computer science 0.21 Cannot be understood by professional linguists who are not authors on the paper 0.10 Cannot be understood by professional linguists who have not recently imbibed mind- altering substances 0.01 Cannot be understood by the author of the paper two hours after writing it
Combining all of these factors, we arrive at the following equation for change in fame as the result of publication:
ΔT = [(FA · √N-1) + τf] · max[(IF · δJ)sC·i , χg!] · [(ΔΘ + ε)-Ω + (√Θ + ε)-√Ω] · [log13(ћ)]-1 - β⦿g1/83.6
Note that ! here indicates the factorial operator, not the imperative mood or an interjection. So, “6!” isn’t “SIX!“, but rather 6 x 5 x 4 x 3 x 2 x 1.
From this equation, we can see that a non-
There are some limitations with our model. We have not dealt with artificial languages and their impact on ћ (though it is of little concern at present). We have not yet incorporated a term for the effect of equations on linguistic fame since the actual occurrence of papers with equations is rare, though the effect is known to be strongly positive. Nor have we fully modeled the effects of non-
Also of note, fame decays over time
T(t) = T0e-λt
where t is measured in issues of all linguistics journals published since the article in question first appeared, and λ is a decay constant that is particular to a given sub-
0.012 Syntax 0.079 Computational Linguistics 0.144 Phonology 0.251 Typology 0.403 Morphology 0.795 Phonetics 1.966 Sociolinguistics
4.533 Historical Linguistics 5.011 Comparative Linguistics 17.45 Semantics 23.29 Pragmatics 50.13 Discourse Analysis 318.9 Applied Linguistics
As we come to the end of this paper, we realize that we may have been too harsh on Slater and Colden, since their first-