[[Hillier_et+al_NarrativeStyleInfluencesCitationFrequency_2016]] # [Narrative Style Influences Citation Frequency in Climate Change Science](https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0167983&type=printable) ## [[Ann Hillier]]; [[Ryan P. Kelly]]; [[Terrie Klinger]] ### 2016 ## Key concepts #claim/climate_science; #science_writing; #narrative_research; #expository_writing ## Quote > Evidence from psychology and literary theory suggests that audiences better understand and remember narrative writing in comparison with expository writing, and new evidence from neuroscience has revealed a specific region in the brain that is activated by stories ## Key points - Climate change is among the most compelling issues confronting science and society, and climate science as a research endeavor has grown over the past decade - 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] - Four of six narrative elements were positively associated with article citation frequency (Fig 1) - 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 - 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 that is identifiable to or understood by the reader ## Synopsis ### Introduction Climate change is among the most compelling issues confronting science and society, and climate science as a research endeavor has grown over the past decade. 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 narratives are widely recognized as powerful tools of communication [^2],[^6] ### 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; the abstract is the only section of the paper immediately available on databases such as PubMed [^22]. We reasoned that it takes a number of years for papers to accrue a number of citations—and 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. Knowing that citations accrue to individual papers nonlinearly over time, we recognized the difficulty in using the available data to derive time-correction factors for each paper in the dataset. We featured only papers from a narrow time window, minimizing the effect of time-since-publication on the distribution of citations in our dataset ### Results Individual Indicators of Narrativity

Four of six narrative elements were positively associated with article citation frequency (Fig 1). 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). We found no citation effect for abstract length after accounting for journal; 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). ### Conclusion 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 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. 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 that is identifiable to or understood by the reader ## Study subjects ### 7 data - 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] ### 7 respondents - 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”) ### 7 data - 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] ## Data analysis - #method/spearman_correlations - #method/spearmans_rho_to_test_for_correlations - #method/pca - #method/continuous_variables - #method/ANOVA - #method/principal_components_analysis - #method/shapiro_wilk_test - #method/linear_regression_to_test - #method/wilcoxon_rank - #method/spearmans - #method/wilcoxon_rank_sum_test_to_test ## Findings - 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 - 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) - 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) - 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) ## Confirmation of earlier findings - 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],[^19],[^20],[^21] ## Limitations - 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. 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