coauthor: Swiss political science co-authorship network 2013

In btergm: Temporal Exponential Random Graph Models by Bootstrapped Pseudolikelihood

Swiss political science co-authorship network 2013

Description

Swiss political science co-authorship network 2013

Format

ch_coaut

is an undirected, weighted 156 x 156 adjancency matrix indicating how many publications political scientist in Switzerland shared with each other as reported in late 2013, including only postdoctoral and professorial political scientists affiliated with research institutes or universities. The exact edge weight should be treated with caution because some publications were counted multiple times because they were reported by multiple co-authors. The diagonal contains the number of publications of the respective author. Leifeld and Ingold (2016) describe the data collection process in more detail.

ch_nodeattr

is a data frame with node attributes/variables for the 156 researchers, in the same alphabetical row order as the network matrix. The first twelve columns with column labels starting with "inst_" are affiliations with different institutions (1 = affiliated; 0 = no affiliation). The next seven columns with column labels starting with "city_" contain the locations of the researchers' institutional affiliations. The "phdyear" column contains the self-reported year of obtaining the PhD, and the "birthyear" column contains the self-reported or publicly available year of birth; these two variables contain many missing values. The "status" column indicates whether a researcher was listed as a professor or as having postdoctoral or other non-professorial status at the time. "chairtitle" is the name of the chair or research group the researcher reported to be a member of. "num_publications" is the total number of publications, "num_articles" the number of journal articles among them, "num_books" the number of books among them, "share_articles" the percentage of journal articles among the publications, and "share_books" the percentage of monographs and edited volumes among the publications. The four columns with column names starting with "lang_" contain the relative shares of English, French, German, Italian, and other languages among the publications of the researcher. The column "share_en_articles" contains the percentage of English journal articles among all publications of the researcher. "male" is a dummy variable indicating whether the author is male (1) or female (0). The variables contained here are described in Leifeld (2018).

ch_dist100km

is a 156 x 156 matrix containing the geographical distance between any two researchers measured in units of 100km (for a reasonable scaling of coefficients in a statistical model), computed over the latitude and longitude of their main institutional affiliations. The measure is included in Leifeld (2018).

ch_en_article_sim

is a 156 x 156 matrix containing the similarity between any two researchers in terms of the share of their work that is published in English and as journal articles. Values closer to 1.0 indicate that two researchers were similar in their language and publication type portfolio while values closer to 0 indicate that they were relatively dissimilar. Only extra-dyadic publications were counted in establishing this similarity. I.e., if researcher A and B co-authored, their joint publications were not included in establishing their English article share similarity. This was done to reduce endogeneity/reverse causality when modeling co-authorship as a function of English article share similarity. The measure is described in Leifeld (2018).

ch_topicsim

is a 156 x 156 topic similarity matrix for the researchers. Topic similarities were computed by taking into account all words in the publication titles of any two researchers, excluding the publications they published as co-authors (i.e., only extra-dyadic publications, to reduce endogeneity/reverse causality in modeling co-authorship ties as a function of topic similarity). Topic similarity was established by computing the cosine similarity between the tf-idf scores for the title words of any two researchers (i.e., a vector space model). Leifeld (2018) contains more details on this procedure.

Details

The Swiss political science co-authorship network 2013 dataset contains the co-authorship network of all political scientists at Swiss universities and research institutes in late 2013. The data are described in Leifeld and Ingold (2016) and Leifeld (2018). The data contained here include postdoctoral and professorial researchers but not PhD students, as in Leifeld (2018), without the PhD researchers included in Leifeld and Ingold (2016). For the full dataset, see the replication archive at DOI \Sexpr[results=rd]{tools:::Rd_expr_doi("10.7910/DVN/85SK1M")}.

Leifeld and Ingold (2016) summarize the data collection strategy as follows: "Data gathering took place between July and December 2013. A single coder pursued a three-step coding procedure: he first created a list of all relevant university departments and research institutes that host political scientists in Switzerland, then he browsed the websites of these institutes and entered all researchers along with several details about them into a database, including their seniority status (predoctoral, postdoctoral, or professor) and the URL of their publication list (either the CV, the institutional website, a private homepage, or several of those items in order to get a complete publication profile of each person). After entering all researchers of an institute, the coder went through the researchers' publication lists and entered the following pieces of information for each publication into the database: the reporting author, the names of all co-authors, the title of the publication, the year, the name of the journal or book in which the publication appeared (if applicable), the names of all editors (if applicable), and a classification of the type of publication (academic journal, book chapter, monograph, edited volume, other). Most publications are relatively recent, but the earliest publications in the database date back to the 1960s. After completing these three steps, data entered at the beginning was double-checked in order to avoid bias due to new publications that may have shown up during the coding time period. This procedure is the best one can do in terms of completeness, but it should be clear that it crucially depends on the accuracy of the self-reported bibliographic information. For example, if a researcher did not update his or her CV or list of publications for the previous six months, those most recent publications only had a chance to enter the database if the co-authors listed the publication on their website. In some relatively rare cases, all authors failed to report recent updates, and this may cause minor inaccuracies in the network dataset, mostly affecting very recent publications in 2013 because there is, on average, a reporting lag."

Based on the collected publication data, a co-authorship network matrix with 156 nodes was created. In addition to this matrix, the dataset here contains node attribute data (institutional affiliations, location, demographics, language shares, publication type shares) and relational covariates (geographical distance, similarity in terms of the share of English articles, and topic similarity) as described in Leifeld (2018). The dataset can be used to replicate Leifeld (2018), but only approximately due to changes in the estimation routine in the ergm package since the article was published.

Source

The data were collected from public information online. The full data collection details are described in Leifeld and Ingold (2016).

References

Leifeld, Philip (2018): Polarization in the Social Sciences: Assortative Mixing in Social Science Collaboration Networks is Resilient to Interventions. Physica A: Statistical Mechanics and its Applications 507: 510–523. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.physa.2018.05.109")}. Full replication data: \Sexpr[results=rd]{tools:::Rd_expr_doi("10.7910/DVN/85SK1M")}.

Leifeld, Philip and Karin Ingold (2016): Co-authorship Networks in Swiss Political Research. Swiss Political Science Review 22(2): 264–287. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/spsr.12193")}.

Examples

## Not run: 
# Replication code for the full Swiss co-authorship ERGM in Leifeld (2018).
# Note that the estimates can only be reproduced approximately due to
# internal changes in the ergm package.

library("network")
library("ergm")

data("ch_coauthor")

# set up network object with node attributes
ch_nw <- network(ch_coaut, directed = FALSE)
set.vertex.attribute(ch_nw, "frequency", ch_nodeattr$num_publications)
set.vertex.attribute(ch_nw, "status", as.character(ch_nodeattr$status))
set.vertex.attribute(ch_nw, "male", ch_nodeattr$male)
set.vertex.attribute(ch_nw, "share_en_articles",
                     ch_nodeattr$share_en_articles)

# create same affiliation matrix
ch_inst_indices <- which(grepl("^inst_.+", colnames(ch_nodeattr)))
ch_same_affiliation <- as.matrix(ch_nodeattr[, ch_inst_indices]) %*%
  t(ch_nodeattr[, ch_inst_indices])

# create same chair matrix
ch_nodeattr$chairtitle[ch_nodeattr$chairtitle == ""] <- NA
ch_same_chair <- matrix(0, nrow = nrow(ch_same_affiliation),
                        ncol = ncol(ch_same_affiliation))
for (i in 1:length(ch_nodeattr$chairtitle)) {
  for (j in 1:length(ch_nodeattr$chairtitle)) {
    if (i != j &&
        !is.na(ch_nodeattr$chairtitle[i]) &&
        !is.na(ch_nodeattr$chairtitle[j]) &&
        ch_nodeattr$chairtitle[i] == ch_nodeattr$chairtitle[j] &&
        ch_same_affiliation[i, j] == TRUE) {
      ch_same_chair[i, j] <- 1
    }
  }
}
rownames(ch_same_chair) <- rownames(ch_same_affiliation)
colnames(ch_same_chair) <- colnames(ch_same_affiliation)

# create supervision matrix (same chair + affiliation + mixed seniority)
ch_supervision <- ch_same_affiliation *
  ch_same_chair *
  matrix(ch_nodeattr$status == "professor",
         nrow = nrow(ch_same_chair),
         ncol = ncol(ch_same_chair),
         byrow = FALSE) *
  matrix(ch_nodeattr$status != "professor",
         nrow = nrow(ch_same_chair),
         ncol = ncol(ch_same_chair),
         byrow = TRUE)

# ERGM estimation
ch_model <- ergm(ch_nw ~
                   edges +
                   gwesp(0.3, fixed = TRUE) +
                   gwdegree(0.4, fixed = TRUE) +
                   nodecov("frequency") +
                   nodefactor("status") +
                   nodefactor("male") +
                   nodematch("male") +
                   edgecov(ch_dist100km) +
                   edgecov(ch_same_affiliation) +
                   edgecov(ch_same_chair) +
                   edgecov(ch_supervision) +
                   edgecov(ch_topicsim) +
                   nodecov("share_en_articles") +
                   edgecov(ch_en_article_sim),
                 control = control.ergm(MCMLE.termination = "Hummel",
                                        MCMLE.effectiveSize = NULL))
summary(ch_model)  # corresponds Column 1 in Table 3 in Leifeld (2018)

## End(Not run)

btergm documentation built on May 29, 2024, 12:09 p.m.