# Code to replicate analyses and figures presented in the article: # Seppänen et al(2020): Co-Citation Percentile Rank and JYUcite: a new network-standardized output-level citation influence metric and its implementation using Dimensions API #Example data and data description are available at library(ggplot2) library(dplyr) library(quantreg) library(betareg) library(DataCombine) library(tidyr) library(sjmisc) library(stringr) library(lmtest) library(jtools) library(multcomp) #Load example data cpr_example_data <- read.csv("cpr_example_data.csv", encoding="UTF-8", stringsAsFactors=TRUE) #Correct 5 cases where citer_count and thus cit_rate were zero, even though citers were found cpr_example_data[which(cpr_example_data$cit_rate==0 & cpr_example_data$citer_count>0) , 'times_cited'] <- cpr_example_data[which(cpr_example_data$cit_rate==0 & cpr_example_data$citer_count>0) , 'citer_count'] cpr_example_data[which(cpr_example_data$cit_rate==0 & cpr_example_data$citer_count>0) , 'cit_rate'] <- cpr_example_data[which(cpr_example_data$cit_rate==0 & cpr_example_data$citer_count>0) , 'citer_count'] / cpr_example_data[which(cpr_example_data$cit_rate==0 & cpr_example_data$citer_count>0) , 'available'] #Attach data to use variables without prefix attach(cpr_example_data) ############### QUANTILE REGRESSION ANALYSES ON... ########################## ### SOLVING TIME QR_solvetime_.50 <- rq(solve_time~times_cited, tau=.5) QR_solvetime_.05 <- rq(solve_time~times_cited, tau=.05) QR_solvetime_.95 <- rq(solve_time~times_cited, tau=.95) predictions.50<-predict(QR_solvetime_.50, newdata=data.frame(times_cited=times_cited)) predictions.05<-predict(QR_solvetime_.05, newdata=data.frame(times_cited=times_cited)) predictions.95<-predict(QR_solvetime_.95, newdata=data.frame(times_cited=times_cited)) CI<-predict(QR_solvetime_.50, newdata=data.frame(times_cited=times_cited), interval="confidence", level=.99) fig2<- ggplot(cpr_example_data, aes(x=times_cited, y=solve_time)) + labs(x ="Times cited", y = "Solve time (seconds)") + theme_bw() + theme(panel.border = element_blank(),axis.line = element_line(colour = "black"), axis.title.x = element_text(color="black", size=30, face="bold", family="serif"), axis.title.y = element_text(color="black", size=30, face="bold", family="serif"),axis.text.x= element_text(color="black", size=20, family="serif"),axis.text.y= element_text(color="black", size=20, family="serif") )+ scale_x_continuous(trans="log10",breaks=c(1,5,10,20,50,100,200,500,1000,2000),labels=c(1,5,10,20,50,100,200,500,1000,2000),minor_breaks=NULL) + scale_y_continuous(trans="log10", breaks=c(1,10,30,60,120,240),labels=c(1,10,30,60,120,240),minor_breaks=NULL) + geom_point(size = 2, shape = 16, fill="black", stroke=FALSE, alpha=0.1) + geom_ribbon(aes(ymin=predictions.05, ymax=predictions.95), alpha=0.05)+geom_line(aes(y = predictions.50))+geom_line(aes(y = CI[,2]), linetype="dashed")+geom_line(aes(y = CI[,3]), linetype="dashed") + coord_cartesian(ylim=c(1, 260)) fig2 summ(QR_solvetime_.50) ### CO-CITATION SET SIZE QR_setsize.50 <- rq(co_cit_size ~ times_cited, tau=.5) QR_setsize.05 <- rq(co_cit_size ~ times_cited, tau=.05) QR_setsize.95 <- rq(co_cit_size ~ times_cited, tau=.95) predictions.50<-predict(QR_setsize.50, newdata=data.frame(times_cited=times_cited)) predictions.95<-predict(QR_setsize.95, newdata=data.frame(times_cited=times_cited)) predictions.05<-predict(QR_setsize.05, newdata=data.frame(times_cited=times_cited)) CI<-predict(QR_setsize.50, newdata=data.frame(times_cited=times_cited), interval="confidence", level=.99) fig3<-ggplot(cpr_example_data, aes(x=times_cited, y=co_cit_size)) + labs(x ="Times cited", y = "Co-citation set size") + theme_bw() + theme(panel.border = element_blank(),axis.line = element_line(colour = "black"), axis.title.x = element_text(color="black", size=30, face="bold", family="serif"), axis.title.y = element_text(color="black", size=30, face="bold", family="serif"),axis.text.x= element_text(color="black", size=20, family="serif"),axis.text.y= element_text(color="black", size=20, family="serif") )+ scale_x_continuous(trans="log10",breaks=c(1,5,10,20,50,100,200,500,1000,2000),labels=c(1,5,10,20,50,100,200,500,1000,2000),minor_breaks=NULL) + scale_y_continuous(trans="log10", breaks=c(1,10,100,1000,10000,60000),labels=c(1,10,100,1000,10000,60000),minor_breaks=NULL) + geom_point(size = 2, shape = 16, fill="black", stroke=FALSE, alpha=0.1) +geom_ribbon(aes(ymin=predictions.05, ymax=predictions.95), alpha=0.05)+geom_line(aes(y = predictions.50))+geom_line(aes(y = CI[,2]), linetype="dashed")+geom_line(aes(y = CI[,3]), linetype="dashed") fig3 summ(QR_setsize.50) ################ BETA REGRESSION ANALYSIS ############################# #Filter out cases where number of co-citations for which citation count could be found in Dimensions database was less than 10 workdata <- cpr_example_data %>% filter(co_cit_counted>9) #Attach data to use variables without prefix attach(workdata) # helper function to scale dependent variable in (0,1) to be used in beta regression transf.betareg <- function(y){ n.obs <- sum(!is.na(y)) (y * (n.obs - 1) + 0.5) / n.obs } # scale cpr variable to be used as dependent variable in beta regression y<-transf.betareg(workdata$cpr/100) #Run simple exploratory beta regression loglog_model<-betareg(y~cit_rate, data=workdata, subset=NULL, na.action=na.exclude, link="loglog") #diagnostic plots #plot(loglog_model) # Exclude cases where Cook's distance > 1 cooks<-cooks.distance(loglog_model) workdata$cooks <- cooks workdata_cooked <- workdata %>% filter(cooks<=1) attach(workdata_cooked) # Run beta regression again, this time with precision parameter phi included, accounting for heteroscedasticity y<-transf.betareg(workdata_cooked$cpr/100) loglog_model<-betareg(y~cit_rate|cit_rate, data=workdata_cooked, subset=NULL, na.action=na.exclude, link="loglog") #diagnostic plots #plot(loglog_model) #Exclude the 405 cases where cpr was zero. 12740 cases remain for the analysis workdata_cooked_unleveraged <- workdata_cooked %>% filter(cpr>0) # Run beta regression again on the final dataset y<-transf.betareg(workdata_cooked_unleveraged$cpr/100) loglog_model<-betareg(y~cit_rate|cit_rate, data=workdata_cooked_unleveraged, subset=NULL, na.action=na.exclude, link="loglog") summary(loglog_model) # get the predicted quantiles loglog_fitted_Quantiles <- predict(loglog_model, workdata_cooked_unleveraged, type="quantile", at = c(0.05,0.5,0.95)) #Plot the data and the regression fit and 90% predicted interva fig4<- ggplot(workdata_cooked_unleveraged, aes(x = cit_rate, y = cpr))+labs(x="Citations / 365 days", y = "Co-citation Percentile Rank")+theme_bw()+theme(panel.border = element_blank(),axis.line = element_line(colour = "black"), axis.title.x = element_text(color="black", size=30, face="bold", family="serif"), axis.title.y = element_text(color="black", size=30, face="bold", family="serif"),axis.text.x= element_text(color="black", size=20, family="serif"),axis.text.y= element_text(color="black", size=20, family="serif")) + geom_point(size = 2, shape = 16, fill="black", stroke=FALSE, alpha=0.1) + scale_x_continuous(trans="log2",breaks=c(0.1,0.5,0.5,1:10,20,40,60),labels=c(0.1,0.5,0.5,1:10,20,40,60),minor_breaks=NULL) + geom_line(aes(y = loglog_fitted_Quantiles[,2]*100)) + geom_ribbon(aes(ymin=loglog_fitted_Quantiles[,1]*100, ymax=loglog_fitted_Quantiles[,3]*100), alpha=0.05)+ coord_cartesian(xlim=c(0.1, 60)) fig4 # Create new dataset where cases are replicated where they have multiple department affiliations, so that case occurs once for each department: # first separate department variable to components dept_workdata <- workdata_cooked_unleveraged %>% separate(department,into=c('dept1','dept2','dept3','dept4'),sep="\\|") #get the count of departments for each case dept_workdata$deptcount<-rowSums(!is.na(dept_workdata[,c('dept1','dept2','dept3','dept4')])) #replicate rows according to department count dept_workdata_sep_rep<-dept_workdata dept_workdata_sep_rep<-dept_workdata_sep_rep[rep(row.names(dept_workdata_sep_rep), dept_workdata_sep_rep$deptcount), ] #create column indicating which dept column shoud be used for each case dept_workdata_sep_rep$whatdept<-lapply(rownames(dept_workdata_sep_rep),FUN = function(row){ifelse(str_contains(row,".1"),'dept2',ifelse(str_contains(row,".2"),'dept3',ifelse(str_contains(row,".3"),'dept4','dept1'))) } ) #create column of correct dept name dept_workdata_sep_rep$department<-unlist(lapply(seq_len(nrow(dept_workdata_sep_rep)), function(x){dept_workdata_sep_rep[x,dept_workdata_sep_rep$whatdept[[x]]]})) #filter out empty names dept_workdata_sep_rep<-dept_workdata_sep_rep %>% filter(department!="") #make department column a factor dept_workdata_sep_rep$department<-factor(dept_workdata_sep_rep$department) #get observation counts per departmet deptcounts<-data.frame(summary(dept_workdata_sep_rep$department)) table1<-deptcounts[order(-deptcounts[,1]),,drop=FALSE] View(table1) #filter our departmentsw with fewer than 100 observations dept_workdata_final<-dept_workdata_sep_rep %>% filter(!department %in% c('Chydenius','Science Museum','Multilingual Centre','Open Science Centre','Open university','Teacher Training School','University services')) #Run beta regression, with interaction between cit_rate and department, and a precision parameter y<-transf.betareg(dept_workdata_final$cpr/100) loglog_model<-betareg(y~cit_rate*department|cit_rate,data=dept_workdata_final, subset=NULL, na.action=na.exclude, link="loglog") # create matrix of predictor value combinations predictors<-expand.grid(department=unique(dept_workdata_final$department),cit_rate=seq(0.1,60,0.1)) # calculate the predicted quantiles loglog_fitted_Quantiles <- predict(loglog_model, dept_workdata_final, type="quantile", at = c(0.05,0.5,0.95)) #Plot the data and the regression fit and 90% predicted interval fig5<- ggplot(dept_workdata_final, aes(x = cit_rate, y = cpr))+labs(x="Citations / 365 days", y = "Co-citation Percentile Rank")+theme_bw()+theme(panel.border = element_blank(),axis.line = element_line(colour = "black"), axis.title.x = element_text(color="black", size=30, face="bold", family="serif"), axis.title.y = element_text(color="black", size=30, face="bold", family="serif"),axis.text.x= element_text(color="black", size=20, family="serif"),axis.text.y= element_text(color="black", size=20, family="serif")) + scale_x_continuous(trans="log2",breaks=c(0.1,0.2,0.5,1,2,5,10,20,50),labels=c('.1','.2','.5',1,2,5,10,20,50),minor_breaks=NULL) + geom_point(size = 2, shape = 16, fill="black", stroke=FALSE, alpha=0.1) + geom_line(aes(y = loglog_fitted_Quantiles[,2]*100)) + geom_ribbon(aes(ymin=loglog_fitted_Quantiles[,1]*100, ymax=loglog_fitted_Quantiles[,3]*100), alpha=0.05)+ coord_cartesian(xlim=c(0.1, 60))+ facet_wrap(~department) fig5 # compare to model without departments nodept_loglog_model<-betareg(y~cit_rate|cit_rate,data=dept_workdata_final, subset=NULL, na.action=na.exclude, link="loglog") lrtest(nodept_loglog_model,loglog_model) # pairwise post-hoc comparisons between departments for regression coefficient of cit_rate table_S1<-summary(glht(loglog_model, linfct = c("`cit_rate:departmentApplied Linguistics` -`cit_rate` = 0", "`cit_rate:departmentBio-environmental` -`cit_rate` = 0", "`cit_rate:departmentBio-environmental` -`cit_rate:departmentApplied Linguistics` = 0", "`cit_rate:departmentBusiness & Economics` -`cit_rate` = 0", "`cit_rate:departmentBusiness & Economics` -`cit_rate:departmentApplied Linguistics` = 0", "`cit_rate:departmentBusiness & Economics` -`cit_rate:departmentBio-environmental` = 0", "`cit_rate:departmentChemistry` -`cit_rate` = 0", "`cit_rate:departmentChemistry` -`cit_rate:departmentApplied Linguistics` = 0", "`cit_rate:departmentChemistry` -`cit_rate:departmentBio-environmental` = 0", "`cit_rate:departmentChemistry` -`cit_rate:departmentBusiness & Economics` = 0", "`cit_rate:departmentEducation` -`cit_rate` = 0", "`cit_rate:departmentEducation` -`cit_rate:departmentApplied Linguistics` = 0", "`cit_rate:departmentEducation` -`cit_rate:departmentBio-environmental` = 0", "`cit_rate:departmentEducation` -`cit_rate:departmentBusiness & Economics` = 0", "`cit_rate:departmentEducation` -`cit_rate:departmentChemistry` = 0", "`cit_rate:departmentHistory & Ethnology` -`cit_rate` = 0", "`cit_rate:departmentHistory & Ethnology` -`cit_rate:departmentApplied Linguistics` = 0", "`cit_rate:departmentHistory & Ethnology` -`cit_rate:departmentBio-environmental` = 0", "`cit_rate:departmentHistory & Ethnology` -`cit_rate:departmentBusiness & Economics` = 0", "`cit_rate:departmentHistory & Ethnology` -`cit_rate:departmentChemistry` = 0", "`cit_rate:departmentHistory & Ethnology` -`cit_rate:departmentEducation` = 0", "`cit_rate:departmentInformation Technology` -`cit_rate` = 0", "`cit_rate:departmentInformation Technology` -`cit_rate:departmentApplied Linguistics` = 0", "`cit_rate:departmentInformation Technology` -`cit_rate:departmentBio-environmental` = 0", "`cit_rate:departmentInformation Technology` -`cit_rate:departmentBusiness & Economics` = 0", "`cit_rate:departmentInformation Technology` -`cit_rate:departmentChemistry` = 0", "`cit_rate:departmentInformation Technology` -`cit_rate:departmentEducation` = 0", "`cit_rate:departmentInformation Technology` -`cit_rate:departmentHistory & Ethnology` = 0", "`cit_rate:departmentLanguage & Communication` -`cit_rate` = 0", "`cit_rate:departmentLanguage & Communication` -`cit_rate:departmentApplied Linguistics` = 0", "`cit_rate:departmentLanguage & Communication` -`cit_rate:departmentBio-environmental` = 0", "`cit_rate:departmentLanguage & Communication` -`cit_rate:departmentBusiness & Economics` = 0", "`cit_rate:departmentLanguage & Communication` -`cit_rate:departmentChemistry` = 0", "`cit_rate:departmentLanguage & Communication` -`cit_rate:departmentEducation` = 0", "`cit_rate:departmentLanguage & Communication` -`cit_rate:departmentHistory & Ethnology` = 0", "`cit_rate:departmentLanguage & Communication` -`cit_rate:departmentInformation Technology` = 0", "`cit_rate:departmentMathematics & Statistics` -`cit_rate` = 0", "`cit_rate:departmentMathematics & Statistics` -`cit_rate:departmentApplied Linguistics` = 0", "`cit_rate:departmentMathematics & Statistics` -`cit_rate:departmentBio-environmental` = 0", "`cit_rate:departmentMathematics & Statistics` -`cit_rate:departmentBusiness & Economics` = 0", "`cit_rate:departmentMathematics & Statistics` -`cit_rate:departmentChemistry` = 0", "`cit_rate:departmentMathematics & Statistics` -`cit_rate:departmentEducation` = 0", "`cit_rate:departmentMathematics & Statistics` -`cit_rate:departmentHistory & Ethnology` = 0", "`cit_rate:departmentMathematics & Statistics` -`cit_rate:departmentInformation Technology` = 0", "`cit_rate:departmentMathematics & Statistics` -`cit_rate:departmentLanguage & Communication` = 0", "`cit_rate:departmentMusic, Arts & Culture` -`cit_rate` = 0", "`cit_rate:departmentMusic, Arts & Culture` -`cit_rate:departmentApplied Linguistics` = 0", "`cit_rate:departmentMusic, Arts & Culture` -`cit_rate:departmentBio-environmental` = 0", "`cit_rate:departmentMusic, Arts & Culture` -`cit_rate:departmentBusiness & Economics` = 0", "`cit_rate:departmentMusic, Arts & Culture` -`cit_rate:departmentChemistry` = 0", "`cit_rate:departmentMusic, Arts & Culture` -`cit_rate:departmentEducation` = 0", "`cit_rate:departmentMusic, Arts & Culture` -`cit_rate:departmentHistory & Ethnology` = 0", "`cit_rate:departmentMusic, Arts & Culture` -`cit_rate:departmentInformation Technology` = 0", "`cit_rate:departmentMusic, Arts & Culture` -`cit_rate:departmentLanguage & Communication` = 0", "`cit_rate:departmentMusic, Arts & Culture` -`cit_rate:departmentMathematics & Statistics` = 0", "`cit_rate:departmentPhysics` -`cit_rate` = 0", "`cit_rate:departmentPhysics` -`cit_rate:departmentApplied Linguistics` = 0", "`cit_rate:departmentPhysics` -`cit_rate:departmentBio-environmental` = 0", "`cit_rate:departmentPhysics` -`cit_rate:departmentBusiness & Economics` = 0", "`cit_rate:departmentPhysics` -`cit_rate:departmentChemistry` = 0", "`cit_rate:departmentPhysics` -`cit_rate:departmentEducation` = 0", "`cit_rate:departmentPhysics` -`cit_rate:departmentHistory & Ethnology` = 0", "`cit_rate:departmentPhysics` -`cit_rate:departmentInformation Technology` = 0", "`cit_rate:departmentPhysics` -`cit_rate:departmentLanguage & Communication` = 0", "`cit_rate:departmentPhysics` -`cit_rate:departmentMathematics & Statistics` = 0", "`cit_rate:departmentPhysics` -`cit_rate:departmentMusic, Arts & Culture` = 0", "`cit_rate:departmentPsychology` -`cit_rate` = 0", "`cit_rate:departmentPsychology` -`cit_rate:departmentApplied Linguistics` = 0", "`cit_rate:departmentPsychology` -`cit_rate:departmentBio-environmental` = 0", "`cit_rate:departmentPsychology` -`cit_rate:departmentBusiness & Economics` = 0", "`cit_rate:departmentPsychology` -`cit_rate:departmentChemistry` = 0", "`cit_rate:departmentPsychology` -`cit_rate:departmentEducation` = 0", "`cit_rate:departmentPsychology` -`cit_rate:departmentHistory & Ethnology` = 0", "`cit_rate:departmentPsychology` -`cit_rate:departmentInformation Technology` = 0", "`cit_rate:departmentPsychology` -`cit_rate:departmentLanguage & Communication` = 0", "`cit_rate:departmentPsychology` -`cit_rate:departmentMathematics & Statistics` = 0", "`cit_rate:departmentPsychology` -`cit_rate:departmentMusic, Arts & Culture` = 0", "`cit_rate:departmentPsychology` -`cit_rate:departmentPhysics` = 0", "`cit_rate:departmentSocial Sciences & Philosophy` -`cit_rate` = 0", "`cit_rate:departmentSocial Sciences & Philosophy` -`cit_rate:departmentApplied Linguistics` = 0", "`cit_rate:departmentSocial Sciences & Philosophy` -`cit_rate:departmentBio-environmental` = 0", "`cit_rate:departmentSocial Sciences & Philosophy` -`cit_rate:departmentBusiness & Economics` = 0", "`cit_rate:departmentSocial Sciences & Philosophy` -`cit_rate:departmentChemistry` = 0", "`cit_rate:departmentSocial Sciences & Philosophy` -`cit_rate:departmentEducation` = 0", "`cit_rate:departmentSocial Sciences & Philosophy` -`cit_rate:departmentHistory & Ethnology` = 0", "`cit_rate:departmentSocial Sciences & Philosophy` -`cit_rate:departmentInformation Technology` = 0", "`cit_rate:departmentSocial Sciences & Philosophy` -`cit_rate:departmentLanguage & Communication` = 0", "`cit_rate:departmentSocial Sciences & Philosophy` -`cit_rate:departmentMathematics & Statistics` = 0", "`cit_rate:departmentSocial Sciences & Philosophy` -`cit_rate:departmentMusic, Arts & Culture` = 0", "`cit_rate:departmentSocial Sciences & Philosophy` -`cit_rate:departmentPhysics` = 0", "`cit_rate:departmentSocial Sciences & Philosophy` -`cit_rate:departmentPsychology` = 0", "`cit_rate:departmentSport & Health` -`cit_rate` = 0", "`cit_rate:departmentSport & Health` -`cit_rate:departmentApplied Linguistics` = 0", "`cit_rate:departmentSport & Health` -`cit_rate:departmentBio-environmental` = 0", "`cit_rate:departmentSport & Health` -`cit_rate:departmentBusiness & Economics` = 0", "`cit_rate:departmentSport & Health` -`cit_rate:departmentChemistry` = 0", "`cit_rate:departmentSport & Health` -`cit_rate:departmentEducation` = 0", "`cit_rate:departmentSport & Health` -`cit_rate:departmentHistory & Ethnology` = 0", "`cit_rate:departmentSport & Health` -`cit_rate:departmentInformation Technology` = 0", "`cit_rate:departmentSport & Health` -`cit_rate:departmentLanguage & Communication` = 0", "`cit_rate:departmentSport & Health` -`cit_rate:departmentMathematics & Statistics` = 0", "`cit_rate:departmentSport & Health` -`cit_rate:departmentMusic, Arts & Culture` = 0", "`cit_rate:departmentSport & Health` -`cit_rate:departmentPhysics` = 0", "`cit_rate:departmentSport & Health` -`cit_rate:departmentPsychology` = 0", "`cit_rate:departmentSport & Health` -`cit_rate:departmentSocial Sciences & Philosophy` = 0", "`cit_rate:departmentTeacher Education` -`cit_rate` = 0", "`cit_rate:departmentTeacher Education` -`cit_rate:departmentApplied Linguistics` = 0", "`cit_rate:departmentTeacher Education` -`cit_rate:departmentBio-environmental` = 0", "`cit_rate:departmentTeacher Education` -`cit_rate:departmentBusiness & Economics` = 0", "`cit_rate:departmentTeacher Education` -`cit_rate:departmentChemistry` = 0", "`cit_rate:departmentTeacher Education` -`cit_rate:departmentEducation` = 0", "`cit_rate:departmentTeacher Education` -`cit_rate:departmentHistory & Ethnology` = 0", "`cit_rate:departmentTeacher Education` -`cit_rate:departmentInformation Technology` = 0", "`cit_rate:departmentTeacher Education` -`cit_rate:departmentLanguage & Communication` = 0", "`cit_rate:departmentTeacher Education` -`cit_rate:departmentMathematics & Statistics` = 0", "`cit_rate:departmentTeacher Education` -`cit_rate:departmentMusic, Arts & Culture` = 0", "`cit_rate:departmentTeacher Education` -`cit_rate:departmentPhysics` = 0", "`cit_rate:departmentTeacher Education` -`cit_rate:departmentPsychology` = 0", "`cit_rate:departmentTeacher Education` -`cit_rate:departmentSocial Sciences & Philosophy` = 0", "`cit_rate:departmentTeacher Education` -`cit_rate:departmentSport & Health` = 0"))) table_S1 # pairwise post-hoc comparisons between departments for regression intercept table_S2<-summary(glht(loglog_model, linfct = c("`departmentApplied Linguistics` - `(Intercept)` = 0", "`departmentBio-environmental` - `(Intercept)` = 0", "`departmentBio-environmental` -`departmentApplied Linguistics` = 0", "`departmentBusiness & Economics` - `(Intercept)` = 0", "`departmentBusiness & Economics` -`departmentApplied Linguistics` = 0", "`departmentBusiness & Economics` -`departmentBio-environmental` = 0", "`departmentChemistry` - `(Intercept)` = 0", "`departmentChemistry` -`departmentApplied Linguistics` = 0", "`departmentChemistry` -`departmentBio-environmental` = 0", "`departmentChemistry` -`departmentBusiness & Economics` = 0", "`departmentEducation` - `(Intercept)` = 0", "`departmentEducation` -`departmentApplied Linguistics` = 0", "`departmentEducation` -`departmentBio-environmental` = 0", "`departmentEducation` -`departmentBusiness & Economics` = 0", "`departmentEducation` -`departmentChemistry` = 0", "`departmentHistory & Ethnology` - `(Intercept)` = 0", "`departmentHistory & Ethnology` -`departmentApplied Linguistics` = 0", "`departmentHistory & Ethnology` -`departmentBio-environmental` = 0", "`departmentHistory & Ethnology` -`departmentBusiness & Economics` = 0", "`departmentHistory & Ethnology` -`departmentChemistry` = 0", "`departmentHistory & Ethnology` -`departmentEducation` = 0", "`departmentInformation Technology` - `(Intercept)` = 0", "`departmentInformation Technology` -`departmentApplied Linguistics` = 0", "`departmentInformation Technology` -`departmentBio-environmental` = 0", "`departmentInformation Technology` -`departmentBusiness & Economics` = 0", "`departmentInformation Technology` -`departmentChemistry` = 0", "`departmentInformation Technology` -`departmentEducation` = 0", "`departmentInformation Technology` -`departmentHistory & Ethnology` = 0", "`departmentLanguage & Communication` - `(Intercept)` = 0", "`departmentLanguage & Communication` -`departmentApplied Linguistics` = 0", "`departmentLanguage & Communication` -`departmentBio-environmental` = 0", "`departmentLanguage & Communication` -`departmentBusiness & Economics` = 0", "`departmentLanguage & Communication` -`departmentChemistry` = 0", "`departmentLanguage & Communication` -`departmentEducation` = 0", "`departmentLanguage & Communication` -`departmentHistory & Ethnology` = 0", "`departmentLanguage & Communication` -`departmentInformation Technology` = 0", "`departmentMathematics & Statistics` - `(Intercept)` = 0", "`departmentMathematics & Statistics` -`departmentApplied Linguistics` = 0", "`departmentMathematics & Statistics` -`departmentBio-environmental` = 0", "`departmentMathematics & Statistics` -`departmentBusiness & Economics` = 0", "`departmentMathematics & Statistics` -`departmentChemistry` = 0", "`departmentMathematics & Statistics` -`departmentEducation` = 0", "`departmentMathematics & Statistics` -`departmentHistory & Ethnology` = 0", "`departmentMathematics & Statistics` -`departmentInformation Technology` = 0", "`departmentMathematics & Statistics` -`departmentLanguage & Communication` = 0", "`departmentMusic, Arts & Culture` - `(Intercept)` = 0", "`departmentMusic, Arts & Culture` -`departmentApplied Linguistics` = 0", "`departmentMusic, Arts & Culture` -`departmentBio-environmental` = 0", "`departmentMusic, Arts & Culture` -`departmentBusiness & Economics` = 0", "`departmentMusic, Arts & Culture` -`departmentChemistry` = 0", "`departmentMusic, Arts & Culture` -`departmentEducation` = 0", "`departmentMusic, Arts & Culture` -`departmentHistory & Ethnology` = 0", "`departmentMusic, Arts & Culture` -`departmentInformation Technology` = 0", "`departmentMusic, Arts & Culture` -`departmentLanguage & Communication` = 0", "`departmentMusic, Arts & Culture` -`departmentMathematics & Statistics` = 0", "`departmentPhysics` - `(Intercept)` = 0", "`departmentPhysics` -`departmentApplied Linguistics` = 0", "`departmentPhysics` -`departmentBio-environmental` = 0", "`departmentPhysics` -`departmentBusiness & Economics` = 0", "`departmentPhysics` -`departmentChemistry` = 0", "`departmentPhysics` -`departmentEducation` = 0", "`departmentPhysics` -`departmentHistory & Ethnology` = 0", "`departmentPhysics` -`departmentInformation Technology` = 0", "`departmentPhysics` -`departmentLanguage & Communication` = 0", "`departmentPhysics` -`departmentMathematics & Statistics` = 0", "`departmentPhysics` -`departmentMusic, Arts & Culture` = 0", "`departmentPsychology` - `(Intercept)` = 0", "`departmentPsychology` -`departmentApplied Linguistics` = 0", "`departmentPsychology` -`departmentBio-environmental` = 0", "`departmentPsychology` -`departmentBusiness & Economics` = 0", "`departmentPsychology` -`departmentChemistry` = 0", "`departmentPsychology` -`departmentEducation` = 0", "`departmentPsychology` -`departmentHistory & Ethnology` = 0", "`departmentPsychology` -`departmentInformation Technology` = 0", "`departmentPsychology` -`departmentLanguage & Communication` = 0", "`departmentPsychology` -`departmentMathematics & Statistics` = 0", "`departmentPsychology` -`departmentMusic, Arts & Culture` = 0", "`departmentPsychology` -`departmentPhysics` = 0", "`departmentSocial Sciences & Philosophy` - `(Intercept)` = 0", "`departmentSocial Sciences & Philosophy` -`departmentApplied Linguistics` = 0", "`departmentSocial Sciences & Philosophy` -`departmentBio-environmental` = 0", "`departmentSocial Sciences & Philosophy` -`departmentBusiness & Economics` = 0", "`departmentSocial Sciences & Philosophy` -`departmentChemistry` = 0", "`departmentSocial Sciences & Philosophy` -`departmentEducation` = 0", "`departmentSocial Sciences & Philosophy` -`departmentHistory & Ethnology` = 0", "`departmentSocial Sciences & Philosophy` -`departmentInformation Technology` = 0", "`departmentSocial Sciences & Philosophy` -`departmentLanguage & Communication` = 0", "`departmentSocial Sciences & Philosophy` -`departmentMathematics & Statistics` = 0", "`departmentSocial Sciences & Philosophy` -`departmentMusic, Arts & Culture` = 0", "`departmentSocial Sciences & Philosophy` -`departmentPhysics` = 0", "`departmentSocial Sciences & Philosophy` -`departmentPsychology` = 0", "`departmentSport & Health` - `(Intercept)` = 0", "`departmentSport & Health` -`departmentApplied Linguistics` = 0", "`departmentSport & Health` -`departmentBio-environmental` = 0", "`departmentSport & Health` -`departmentBusiness & Economics` = 0", "`departmentSport & Health` -`departmentChemistry` = 0", "`departmentSport & Health` -`departmentEducation` = 0", "`departmentSport & Health` -`departmentHistory & Ethnology` = 0", "`departmentSport & Health` -`departmentInformation Technology` = 0", "`departmentSport & Health` -`departmentLanguage & Communication` = 0", "`departmentSport & Health` -`departmentMathematics & Statistics` = 0", "`departmentSport & Health` -`departmentMusic, Arts & Culture` = 0", "`departmentSport & Health` -`departmentPhysics` = 0", "`departmentSport & Health` -`departmentPsychology` = 0", "`departmentSport & Health` -`departmentSocial Sciences & Philosophy` = 0", "`departmentTeacher Education` - `(Intercept)` = 0", "`departmentTeacher Education` -`departmentApplied Linguistics` = 0", "`departmentTeacher Education` -`departmentBio-environmental` = 0", "`departmentTeacher Education` -`departmentBusiness & Economics` = 0", "`departmentTeacher Education` -`departmentChemistry` = 0", "`departmentTeacher Education` -`departmentEducation` = 0", "`departmentTeacher Education` -`departmentHistory & Ethnology` = 0", "`departmentTeacher Education` -`departmentInformation Technology` = 0", "`departmentTeacher Education` -`departmentLanguage & Communication` = 0", "`departmentTeacher Education` -`departmentMathematics & Statistics` = 0", "`departmentTeacher Education` -`departmentMusic, Arts & Culture` = 0", "`departmentTeacher Education` -`departmentPhysics` = 0", "`departmentTeacher Education` -`departmentPsychology` = 0", "`departmentTeacher Education` -`departmentSocial Sciences & Philosophy` = 0", "`departmentTeacher Education` -`departmentSport & Health` = 0"))) table_S2