EquationCitations: Number of Equations and Citations for Evolutionary Biology...
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EquationCitations

R Documentation

Number of Equations and Citations for Evolutionary Biology Publications

Description

Analysis of citations of evolutionary biology papers published in 1998 in the top three journals (as judged by their 5-year impact factors in the Thomson Reuters Journal Citation Reports 2010).

Usage

data("EquationCitations")

Format

A data frame containing 649 observations on 13 variables.

journal: Factor. Journal in which the paper was published (The American Naturalist, Evolution, Proceedings of the Royal Society of London B: Biological Sciences).
authors: Character. Names of authors.
volume: Volume in which the paper was published.
startpage: Starting page of publication.
pages: Number of pages.
equations: Number of equations in total.
mainequations: Number of equations in main text.
appequations: Number of equations in appendix.
cites: Number of citations in total.
selfcites: Number of citations by the authors themselves.
othercites: Number of citations by other authors.
theocites: Number of citations by theoretical papers.
nontheocites: Number of citations by nontheoretical papers.

Details

Fawcett and Higginson (2012) investigate the relationship between the number of citations evolutionary biology papers receive, depending on the number of equations per page in the cited paper. Overall it can be shown that papers with many mathematical equations significantly lower the number of citations they receive, in particular from nontheoretical papers.

Source

Online supplements to Fawcett and Higginson (2012).

https://www.pnas.org/doi/suppl/10.1073/pnas.1205259109/suppl_file/sd01.xlsx

References

Fawcett, T.W. and Higginson, A.D. (2012). Heavy Use of Equations Impedes Communication among Biologists. PNAS – Proceedings of the National Academy of Sciences of the United States of America, 109, 11735–11739. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1073/pnas.1205259109")}

Examples


## load data and MASS package
data("EquationCitations", package = "AER")
library("MASS")

## convenience function for summarizing NB models
nbtable <- function(obj, digits = 3) round(cbind(
  "OR" = exp(coef(obj)),
  "CI" = exp(confint.default(obj)),
  "Wald z" = coeftest(obj)[,3],
  "p" = coeftest(obj)[, 4]), digits = digits)


#################
## Replication ##
#################

## Table 1
m1a <- glm.nb(othercites ~ I(equations/pages) * pages + journal,
  data = EquationCitations)
m1b <- update(m1a, nontheocites ~ .)
m1c <- update(m1a, theocites ~ .)
nbtable(m1a)
nbtable(m1b)
nbtable(m1c)

## Table 2
m2a <- glm.nb(
  othercites ~ (I(mainequations/pages) + I(appequations/pages)) * pages + journal,
  data = EquationCitations)
m2b <- update(m2a, nontheocites ~ .)
m2c <- update(m2a, theocites ~ .)
nbtable(m2a)
nbtable(m2b)
nbtable(m2c)


###############
## Extension ##
###############

## nonlinear page effect: use log(pages) instead of pages+interaction
m3a <- glm.nb(othercites ~ I(equations/pages) + log(pages) + journal,
  data = EquationCitations)
m3b <- update(m3a, nontheocites ~ .)
m3c <- update(m3a, theocites ~ .)

## nested models: allow different equation effects over journals
m4a <- glm.nb(othercites ~ journal / I(equations/pages) + log(pages),
  data = EquationCitations)
m4b <- update(m4a, nontheocites ~ .)
m4c <- update(m4a, theocites ~ .)

## nested model best (wrt AIC) for all responses
AIC(m1a, m2a, m3a, m4a)
nbtable(m4a)
AIC(m1b, m2b, m3b, m4b)
nbtable(m4b)
AIC(m1c, m2c, m3c, m4c)
nbtable(m4c)
## equation effect by journal/response
##           comb nontheo theo
## AmNat     =/-  -       +
## Evolution =/+  =       +
## ProcB     -    -       =/+

AER documentation built on Sept. 28, 2024, 9:07 a.m.