Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Countries citing papers authored by Jennifer Culbertson
Since
Specialization
Citations
This map shows the geographic impact of Jennifer Culbertson's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Jennifer Culbertson with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jennifer Culbertson more than expected).
Fields of papers citing papers by Jennifer Culbertson
This network shows the impact of papers produced by Jennifer Culbertson. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jennifer Culbertson. The network helps show where Jennifer Culbertson may publish in the future.
Co-authorship network of co-authors of Jennifer Culbertson
This figure shows the co-authorship network connecting the top 25 collaborators of Jennifer Culbertson.
A scholar is included among the top collaborators of Jennifer Culbertson based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Jennifer Culbertson. Jennifer Culbertson is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Martin, Alexander, Klaus Abels, David Adger, & Jennifer Culbertson. (2019). Do learners' word order preferences reflect hierarchical language structure?. Edinburgh Research Explorer (University of Edinburgh). 2303–2309.4 indexed citations
7.
Culbertson, Jennifer, et al.. (2019). Something about "us": Learning first person pronoun systems.. Cognitive Science. 749–755.6 indexed citations
8.
Oseki, Yohei, et al.. (2019). Do cross-linguistic patterns of morpheme order reflect a cognitive bias?. Cognitive Science. 994–1000.2 indexed citations
9.
Smith, Kenny, et al.. (2017). Is the strength of regularisation behaviour uniform across linguistic levels. Cognitive Science. 1023–1028.2 indexed citations
10.
Smith, Kenny, Olga Fehér, & Jennifer Culbertson. (2017). The influence of word-order harmony on structural priming in artificial languages.. Cognitive Science.1 indexed citations
11.
Smith, Kenny, et al.. (2017). Language-users choose short words in predictive contexts in an artificial language task.. Cognitive Science.1 indexed citations
12.
Schouwstra, Marieke, Simon Kirby, & Jennifer Culbertson. (2017). Silent gesture and noun phrase universals. Cognitive Science. 3095–3100.1 indexed citations
13.
Schouwstra, Marieke, et al.. (2017). The cultural evolution of complex linguistic constructions in artificial sign languages. Cognitive Science.7 indexed citations
14.
Culbertson, Jennifer, et al.. (2017). Harmony in a non-harmonic language: word order learning in French children.. Cognitive Science.2 indexed citations
Culbertson, Jennifer. (2010). Learning biases, regularization, and the emergence of typological universals in syntax.6 indexed citations
20.
Culbertson, Jennifer & Steven Gross. (2009). Are Linguists Better Subjects?. The British Journal for the Philosophy of Science. 60(4). 721–736.34 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.