Sunday, 1 January 2017

Narrative Style Influences Citation Frequency in Climate Change Science

 Source: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0167983





















Abstract

Peer-reviewed
publications focusing on climate change are growing exponentially with
the consequence that the uptake and influence of individual papers
varies greatly. Here, we derive metrics of narrativity from psychology
and literary theory, and use these metrics to test the hypothesis that
more narrative climate change writing is more likely to be influential,
using citation frequency as a proxy for influence. From a sample of 732
scientific abstracts drawn from the climate change literature, we find that articles with more narrative abstracts are cited more often.
This effect is closely associated with journal identity: higher-impact
journals tend to feature more narrative articles, and these articles
tend to be cited more often. These results suggest that writing in a
more narrative style increases the uptake and influence of articles in
climate literature, and perhaps in scientific literature more broadly.


Citation: Hillier A, Kelly RP, Klinger T (2016) Narrative Style Influences Citation Frequency in Climate Change Science. PLoS ONE 11(12):
e0167983.
doi:10.1371/journal.pone.0167983

Editor: Gary S. Bilotta, University of Brighton, UNITED KINGDOM

Received: September 7, 2016; Accepted: November 23, 2016; Published: December 15, 2016

Copyright: © 2016 Hillier et al. This is an open access article distributed under the terms of the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any
medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Climate
change is among the most compelling issues now confronting science and
society, and climate science as a research endeavor has grown
accordingly over the past decade. The number of scholarly publications
is increasing exponentially, doubling every 5–6 years [1].
The volume of climate science publications now being produced far
exceeds the ability of individual investigators to read, remember, and
use. Accordingly, it is increasingly important that individual articles
be presented in a way that facilitates the uptake of climate science and
increases the salience of their individual research contributions.


Evidence
from psychology and literary theory suggests that audiences better
understand and remember narrative writing in comparison with expository
writing [2,3], and new evidence from neuroscience has revealed a specific region in the brain that is activated by stories [4]. Narrative writing tells a story through related events [5],
whereas expository writing relates facts without much social context.
Presenting the same information in a more narrative way has the
potential to increase its uptake—an especially attractive prospect in
the context of climate science and scientific writing generally—and
consequently, narratives are widely recognized as powerful tools of
communication [2,6].


Despite
this, professional scientific writing tends to be more expository than
narrative, prioritizing objective observations made by detached
researchers and relying on the logical proposition “if X, then Y” to
define the structure of the argument [7]. Narrative writing, on the other hand, is commonly used to good effect in popular science writing [8]. Both simple narratives and apocalyptic climate narratives are known to capture public attention and spur action [9–11]. Moreover, narratives can influence perceptions of climate risk and policy preferences among the public [12],
and the narrative style has been proposed as a powerful means of
research to address problems of knowledge, policy, and action as they
relate to climate change [13].


Here
we explore the influence of narrative in the professional communication
of climate science research, acknowledging that the perception of
narrative can be subjective and context-dependent [14,15].
We hypothesized that scientific papers with more narrative text are
more likely to be highly cited than those with less narrative (i.e.,
more expository) text, using citation frequency as a proxy for a paper’s
influence on the field at large. To test this hypothesis, we derived
six elements of narrativity from studies on narrative comprehension [15–17] and the literatures of psychology [2,18,19] and narrative theory [14,20,21],
and used these six elements to evaluate the degree of narrativity in
732 abstracts taken from the peer-reviewed scientific literature on
climate change. We then assessed the relationship between narrativity in
these journal abstracts in the context of other factors known to
influence citation rate, including journal identity, abstract length,
and number of authors.


Methods


Abstract Selection

We
analyzed abstracts instead of the full text of selected papers because
the abstract typically is the first section of the paper viewed by
readers; moreover, the abstract is the only section of the paper
immediately available on databases such as PubMed [22].
Hence, abstracts provide a relatively consistent point of entry to
scientific publications. To select focal abstracts for the dataset, we
first used the PubMed database to select the journals that published the
largest number of articles featuring the phrase “climate change” in the
abstract or title between 2009 and 2010. Our reasoning for choosing the
set of papers that we did was as follows: First, we limited the scope
by the field of inquiry (climate change), hoping to minimize the
statistical variance (or “noise”) that would probably have resulted from
an analysis that included many fields (which in turn likely differ in
citation frequencies and writing conventions, among other relevant
factors). Next, we reasoned that it takes a number of years for papers
to accrue a number of citations—and consequently for a set of papers to
develop a distribution of citation counts—that would allow us to test
our core hypothesis. We began this study in 2015, and chose 5-to-6 years
as a reasonable window, allowing for citations to accrue, but not
letting the papers become outdated. Finally, knowing that citations
accrue to individual papers nonlinearly over time, we recognized the
difficulty in using the available data (total citations, rather than
citations-by-year for each paper) to derive time-correction factors for
each paper in the dataset. Consequently, we featured only papers from a
narrow time window, minimizing the effect of time-since-publication on
the distribution of citations in our dataset.


We
identified 19 journals with the largest number of articles meeting
these criteria, and then retrieved the abstracts, citation counts, and
other relevant information through the database Web of Science (S1 Table;
raw dataset N = 802 abstracts; N = 732 after quality control; see
below). These abstracts differed in citation frequency by two orders of
magnitude, having been cited between 1 and 1205 times as of March 30,
2016 (median = 69; we did not collect data on papers with zero citations
in order to avoid the problems associated with log-transforming zero
data), and reflected the expected left-skewed distribution.



Crowdsourcing

We used the crowdsourcing site CrowdFlower (http://www.crowdflower.com)
to collect information regarding the narrativity of each abstract.
Crowdsourcing—in which many individuals are paid small amounts of money
to complete discrete parts of a much larger task—as a research method is
growing as technical capacity increases [23].
It offers an efficient research tool for work that requires a degree of
human assessment spread over a large number of data points, with access
to a diverse, skilled workforce, and produces reliable data in
comparison with alternative methods [24,25].


The
CrowdFlower platform allowed us to: 1) collect reader-coded information
for a large number of abstracts that could not be collected by
text-mining or other means; 2) collect multiple (n = 7) independent
assessments (“judgments”) about the narrativity of each abstract; and
simultaneously 3) include human interpretation and discretion in the
quantification of narrativity. We collected multiple judgments for each
abstract as a means of quality-control, given that individual readers
can perceive narrativity somewhat differently [26].


Online contributors evaluated abstracts by first reading instructions (S1 Text) and an example question, and then answering a series of six questions (S2 Text)
for each abstract. These questions were intended to evaluate each
abstract with respect to indicators of narrativity (described in the
next section). Contributors were paid per submitted page, each of which
included five abstracts and the corresponding questions.


We
used the following measures to ensure high quality responses: 1) gave
access to this job only to CrowdFlower’s highest ranked contributors
(the site ranks them based upon past performance); 2) set a minimum
completion time for each page of work; and 3) restricted contributor
location to a number of countries in which English is the primary
language and literacy rates are high: Australia, Canada, New Zealand,
United Kingdom, and United States. Although our primary reason for
imposing this restriction was based on language skills, we note that
these countries largely correspond to those that dominate climate change
publications, both in terms of number and citation frequency [1]. A total of 155 individual contributors evaluated the abstracts used in this study.



Independent Variables: Narrative Indicators

To derive indicators of narrativity, we adapted methods and indicators based on comparable studies [15–17] and supported by relevant literature from narrative theory [14,20,21], psychology [2,18,19], communications [27], philosophy [28], and history [26]. We chose indicators to reflect setting, narrative perspective, sensory language, conjunctions, connectivity, and appeal.


Setting
provides a description of where and when events take place and is of
the fundamental components of narratives. The spatial and temporal
dimensions established by setting help create a mental image that
distinguishes narratives from other forms of discourse [20]. We assessed setting by asking contributors whether there is a specific mention of place or time in the abstract [16].


Narrative perspective describes the position or role of the narrator. According to Lejano et al. [15],
the presence of a narrator distinguishes narratives from other forms of
communication—that is, narrators tell narratives. The narrator is
responsible for eliciting emotions in the reader [29].
First-person narrators have a stronger narrative presence than other
narrative perspectives, such as third-person or no narrator [2,16].
We assessed narrative perspective by asking contributors whether or not
the narrator referred to himself in the text (e.g., through use of
pronouns such as I, we, and our).


Sensory language
appeals to the senses and emotions of the reader and can be used to
establish personal identity, for example, through the narrator
expressing “emotions, attitudes, beliefs, and interpretations” [20].
Accordingly, we assessed sensory language by asking contributors to
count the number of times that sensory or emotional language appeared in
the abstract. We then normalized the resulting counts by abstract
length (number of words).


Conjunctions are used to connect words and phrases, binding narratives together in a logical form [17].
We used the presence of conjunctions to determine the extent to which
an abstract is logically ordered, based on the observation that a
temporal or causal ordering of events is an essential, and
distinguishing, characteristic of narratives [15,30–33], one which implies momentum towards completion [20] and evokes human understanding [21].
We assessed the use of conjunctions by asking contributors to count the
number of times that conjunctions signifying cause and effect,
contrast, or temporal ordering appeared in the text. We then normalized
the resulting counts by abstract length.


Connectivity
refers to words or phrases that create explicit links within the text,
either as a specific reference back to the same thing or repetition of a
word from the previous sentence, provided it carries the same meaning [17].
We assessed connectivity by asking contributors to count the number of
times that words or phrases from one sentence were used to create an
explicit link to the sentence immediately preceding it. We provided
contributors the additional instruction to look for logical linkage
between ideas. We then normalized the resulting counts by abstract
length.


Appeal refers to the moral or evaluative orientation of a narrative [22]. Appeal in the form of evaluative commentary or ‘landscape of consciousness’ is an important aspect of narrativity [14,21], answering the question of why
the story is being told. We assessed the use of appeal by asking
contributors if the text makes an explicit appeal to the reader or a
clear recommendation for action [16].



Independent Variables: Other

In
addition to the crowdsourced assessments of narrative elements, we
collected information on length of abstract (number of words), number of
authors, year of publication, journal identity, and journal impact
factor. These factors are known to influence the citation rate of
peer-reviewed literature [34–36] and were available via Web of Science for each abstract in the dataset.



Dependent Variable: Citation Frequency

We
used citation frequency as a measure of article influence. A large body
of literature supports the use of citation analyses as frameworks for
evaluating science communication [34,36–38].
Citations reflect the cumulative nature of science and the extent to
which a piece of work is represented in a body of literature [36],
and can therefore be used as to evaluate the degree of influence of a
publication on its field. We used Web of Science to establish the number
of citations for the articles associated with each abstract in our
dataset. We log-transformed citation counts to account for the skewed
distribution in citations.



Quality Control

We
treated Question 2, “Does the narrator refer to himself in the text?”
as a “test” question, or secondary quality-control mechanism, due to its
objectivity (i.e., unlike some of the other narrative indicators, the
existence of a first-person narrator has a “true” answer). After
considering all seven responses for this question, respondents who
answered in the majority were included in the analysis, whereas
respondents who answered in the minority were assumed to be in error and
their responses were omitted entirely from the analysis. This improved
our confidence in the responses and subsequent analysis. After omitting
these minority responses, we averaged the scores across remaining
responses for each independent variable to yield a dataset with one
value per indicator for each abstract.


Narrative
variables with “yes/no” categorical responses (i.e., the indicators
“setting”, “narrative perspective”, and “appeal”) were assigned numeric
binary values (0 or 1) by rounding respondents’ mean scores (e.g., where
5 out of 7 respondents scored an abstract as having a direct appeal to
the reader, the mean appeal score for the abstract was 5/7, or 0.71, and
we rounded this score to 1 to reflect the idea that the abstract did
indeed contain a direct appeal). We used the mean response scores for
the other, non-binary narrative variables (“conjunctions”,
“connectivity”, and “sensory”). This turned an otherwise discrete
variable into a continuous variable, creating an index that captured
variations in perceptions of narrativity. For example, contributors
might count different numbers of connective phrases and links in a piece
of text. Taking the mean, and thereby including the disagreement among
responses, produced an overall measure of perceived connectivity for
that piece of text. These methods incorporated the subjective nature of
narrativity into the results.



Analysis

Three
of our narrative elements were binary, and we therefore used a Wilcoxon
Rank-Sum test to test for an association between the presence of these
elements and a change in citation frequency. The remaining three
narrative elements were continuous variables with non-normal
distributions (Shapiro-Wilk test; p < 0.001), and accordingly we used
the nonparametric Spearman’s rho to test for correlations between these
elements and citation frequency.


In
order to account for co-linearities among our narrative elements, we
used a principal components analysis to create a single index of
narrativity. PC1 alone explained 76.5% of the variance in the narrative
elements, with PC2 explaining an additional 13.8%. PCA loadings are
given in S2 Table. All analyses were carried out in R [39],
and the analysis script and raw dataset are available in supporting
files. We also analyzed a version of the same dataset omitting extreme
values in both dependent and independent variables (S1 Fig) obtaining nearly identical results as we report here for the full dataset.


Finally,
we used simple and multiple linear regression to test for significant
associations between groups of variables and citations, and to
illustrate the correlation between our narrative index (PC1) and journal
impact factor.


Results


Individual Indicators of Narrativity

Four of six narrative elements were positively associated with article citation frequency (Fig 1). We obtained similar results when holding the year of publication constant (S2 and S3 Figs) and when analyzing the same dataset with outliers excluded (S1 Fig), indicating that neither publication year nor extreme data points substantially affect the trends we report here. Table 1
shows p-values for nonparametric tests (Wilcoxon Rank Sum for binary
variables; Spearman correlations for continuous variables), and gives
Spearman’s rho for continuous variables.


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Fig 1.
Multipanel plot depicting the relationship between narrativity
(individual indicators and single narrativity index given by PC1,
labeled individually) and article citation frequency.
The
use of sensory language, conjunctions, connectivity, and appeal to the
reader are significantly correlated with article citation frequency. PC1
index of narrativity is significantly correlated with article citation
frequency (linear regression; shaded area indicates 95% confidence
interval for the linear model parameters).



http://dx.doi.org/10.1371/journal.pone.0167983.g001
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Table 1. Nonparametric relationships between each narrative element and log(citations).
For
continuous variables, spearman correlations are given along with
associated p-values. For binary variables, p-values are given for
Wilcoxon rank-sum tests.



http://dx.doi.org/10.1371/journal.pone.0167983.t001
Following
ordination of the six narrative elements using PCA, PC1 served as our
index of narrativity, and was significantly correlated with
log(citations) (R2 = 0.05, p = 10−9; Fig 1). PC1 (Narrativity index) varied significantly among journals (p = 10−15), and correlated strongly and positively with log journal impact factor (R2 = 0.62, p = 6 x 10−5;
carried out on PC1 journal means to avoid pseudoreplication), such that
higher-impact journals tended to have more narrative articles (Fig 2).


thumbnail
Download:
Fig 2. The relationship between the narrativity index (PC1) and journal impact factor.
Response
variables reflect journal means for articles in our dataset (N = 732);
shaded area represents the 95% confidence interval for the best-fit
line. Linear regression R2 = 0.62, p = 6 x 10−5.



http://dx.doi.org/10.1371/journal.pone.0167983.g002

Non-narrative Independent Variables

We
found no citation effect for abstract length after accounting for
journal (different journals feature abstracts of different lengths);
papers with more authors had subtly, but significantly, more citations
than those with fewer authors even after controlling for journal (log(N
authors), p < 10−6; each additional author was associated
with an additional 0.4 citations in the dataset). Citations varied
predictably by journal (ANOVA; R2 = 0.43; p < 10−15), and this effect was largely captured by journal impact factor (R2 = 0.37; p < 10−15; log(2010 impact factor)). Year of publication (2009 vs. 2010) had a small but significant effect on citations (R2 = 0.05; p = 10−10; the average paper from 2010 had 1.5 fewer citations than the average paper from 2009).



Multiple Linear Regression

The
best multiple linear regression model included Year, PC1 (narrativity
index), (log) Number of Authors, and (log) Impact Factor as independent
variables following stepwise model selection using AIC. Taken together,
these variables explained 41% of the variance in citations for our
dataset (p < 10−15).


Discussion

Our
results reveal that—at least among the set of peer-reviewed climate
change literature included in our dataset—articles featuring more
narrative writing styles are more often cited. This effect is
independent of year of publication, number of authors, or abstract
length. Of the narrative elements we tested for, the use of sensory
language, conjunctions, connectivity between sentences, and appeal (or
plea) to the reader all positively and significantly influenced citation
frequency. Of these four attributes, appeal [i.e., to the reader] is
perhaps most broadly construed and least understood. Nevertheless, the
fact that appeal emerged as a key factor in the PCA suggests its
importance in climate science writing. It could be the case that appeal
is positively associated with narrativity because, in the context of
climate science, authors are likely to offer a recommendation (where
recommendation is one definition of the term) that is identifiable to or
understood by the reader.


Our
findings are consistent with the prevailing understanding across a
range of fields that audiences tend to understand and recall
narratives—that is, stories—far better than information received in
other ways [2,14,18–21].
The result is surprising, though, in the context of professional
scientific communication, in which expository styles dominate the
published literature, word counts are strictly limited by editorial
policies, graphics are routinely used to present results, and citation
frequency is often considered to depend largely—even primarily—upon the
strength of the science. These conventions and constraints would seem to
eliminate any role for narrativity in professional scientific writing,
but our results indicate otherwise.


Despite
the significant effect of narrative style, we found the journal of
publication—particularly as captured by the journal’s impact factor—was
most closely associated with citation frequency of individual articles.
However, we found an unexpectedly strong correlation between narrativity
and journal impact factor: more highly cited journals feature more
narrative writing styles. We might speculate that this effect stems from
differences in editorial policies that subtly encourage or discourage
narrative styles, or that, especially in the case of Nature and Science,
effectively communicating to a highly interdisciplinary audience
requires a more narrative style. It may also be that more senior
authors—presumably publishing in higher-impact journals more often—feel
freer to write in a more narrative style. Whatever the reason, the
message to authors is clear: up to a point, more narrative writing
styles can increase the uptake and ultimate visibility of one’s
research.


Our
study design did not allow us to test the mechanism(s) of association
between narrativity and citation frequency. However, our results add to a
growing literature that underscores an important role for narrative
communication structure in readers’ abilities to process and recall
information. Without knowing the specific cognitive mechanism(s)
involved, it appears that the uptake and subsequent use of scientific
information is positively influenced by narrative writing styles.


Peer-reviewed
scientific discourse is often viewed as a special form of
communication, exempt from the qualities of narratives that humans
inherently relate to. However, our findings support an alternative
interpretation: scientists can engage readers and increase uptake by
incorporating narrative attributes into their writing styles. Among the
variables we tested, connectedness, or the extent to which sentences are
logically related, has the greatest positive influence. Moreover, the
use of evaluative commentary can be used to positive effect. By
incorporating such attributes into their writing, scientists can more
closely mirror the way we as humans experience and understand the world.


Supporting Information

S1 Table.docx
(DOCX)

S1 Table. Source journals included in this study.

doi:10.1371/journal.pone.0167983.s001
(DOCX)

S2 Table. Summary of principal components analysis of narrative elements.

doi:10.1371/journal.pone.0167983.s002
(DOCX)

S1 Text. CrowdFlower job instructions.

doi:10.1371/journal.pone.0167983.s003
(DOCX)

S2 Text. CrowdFlower job questions.

doi:10.1371/journal.pone.0167983.s004
(DOCX)

S1 Fig. Multipanel
plot depicting the relationship between narrativity (single variables
and composite index panels, as labeled) and log article citation
frequency with the 46 outlier abstracts removed.

We
identified outlier abstracts by fitting appropriate probability
distributions to the non-binary independent variables (“conjunctions”
(per abstract word), “connectivity” (per abstract word), log(abstract
length), log(number of authors); gamma, gamma, normal, and gamma
distributions, respectively) and to the dependent variable
(log(citations); normal), and excluding responses with a likelihood <
0.01. Consequently, abstracts with very large or very small numbers of
conjunctions or connective phrases—or extreme values for word count,
number of authors, or number of citations—were removed from the dataset.
In total, 46 outliers were removed from the dataset. This figure shows
the results of the analyses described in the main paper, but carried out
on this dataset with the 46 outlier abstracts removed.
doi:10.1371/journal.pone.0167983.s005
(TIFF)

S2 Fig. Multipanel
plot depicting the relationship between narrativity (single variables
and composite index panels, as labeled) and log article citation
frequency for publication year 2009.

doi:10.1371/journal.pone.0167983.s006
(TIFF)

S3 Fig. Multipanel
plot depicting the relationship between narrativity (single variables
and composite index panels, as labeled) and log article citation
frequency for publication year 2010.

doi:10.1371/journal.pone.0167983.s007
(TIFF)

S1 Script. R script used in the analysis.

doi:10.1371/journal.pone.0167983.s008
(R)

S1 Dataset. Raw dataset used in the analysis.

doi:10.1371/journal.pone.0167983.s009
(CSV)

S1 Impact. Journal dataset used in the analysis.

doi:10.1371/journal.pone.0167983.s010
(CSV)

Author Contributions



  1. Conceptualization: AH RPK TK.
  2. Data curation: AH RPK TK.
  3. Formal analysis: RPK.
  4. Investigation: AH RPK.
  5. Methodology: AH RPK TK.
  6. Project administration: AH RPK TK.
  7. Resources: TK.
  8. Software: RPK.
  9. Supervision: RPK TK.
  10. Validation: RPK.
  11. Visualization: AH RPK.
  12. Writing – original draft: AH RPK TK.
  13. Writing – review & editing: AH RPK TK.

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Narrative Style Influences Citation Frequency in Climate Change Science
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