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.
Automated Evaluation of Text and Discourse with Coh-Metrix
2014586 citationsDanielle S. McNamara, Arthur C. Graesser et al.profile →
MTLD, vocd-D, and HD-D: A validation study of sophisticated approaches to lexical diversity assessment
2010514 citationsPhilip M. McCarthy et al.profile →
Linguistic Features of Writing Quality
2009367 citationsDanielle S. McNamara, Scott A. Crossley et al.profile →
Citations per year, relative to Philip M. McCarthy Philip M. McCarthy (= 1×)
peers
Kristopher Kyle
Countries citing papers authored by Philip M. McCarthy
Since
Specialization
Citations
This map shows the geographic impact of Philip M. McCarthy'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 Philip M. McCarthy with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Philip M. McCarthy more than expected).
Fields of papers citing papers by Philip M. McCarthy
This network shows the impact of papers produced by Philip M. McCarthy. 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 Philip M. McCarthy. The network helps show where Philip M. McCarthy may publish in the future.
Co-authorship network of co-authors of Philip M. McCarthy
This figure shows the co-authorship network connecting the top 25 collaborators of Philip M. McCarthy.
A scholar is included among the top collaborators of Philip M. McCarthy 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 Philip M. McCarthy. Philip M. McCarthy is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
McCarthy, Philip M., et al.. (2013). Contrastive Corpus Analysis on the Writing of American and Korean Scientists using Gramulator. Seoul National University Open Repository (Seoul National University). 49(3). 681–702.2 indexed citations
3.
McCarthy, Philip M., et al.. (2012). The Devil Is in the Details: New Directions in Deception Analysis.. The Florida AI Research Society. 188–195.2 indexed citations
4.
McCarthy, Philip M., et al.. (2011). Analyzing journal abstracts written by Japanese, American, and British scientists using Coh-Metrix and the Gramulator. SHILAP Revista de lepidopterología.
5.
McCarthy, Philip M., et al.. (2011). The Hierarchy of Detective Fiction: A Gramulator Analysis. The Florida AI Research Society.1 indexed citations
6.
McCarthy, Philip M., et al.. (2011). Bias in Hard News Articles from Fox News and MSNBC: An Empirical Assessment Using the Gramulator. The Florida AI Research Society.1 indexed citations
7.
McCarthy, Philip M., et al.. (2011). Automatic Natural Language Processing and the Detection of Reading Skills and Reading Comprehension. The Florida AI Research Society. 234–239.2 indexed citations
8.
Weston, Jennifer L., Scott A. Crossley, Philip M. McCarthy, & Danielle S. McNamara. (2011). Number of Words Versus Number Ideas: Finding a Better Predictor of Writing Quality. The Florida AI Research Society. 335–340.3 indexed citations
9.
McCarthy, Philip M.. (2010). GPAT: A Genre Purity Assessment Tool. The Florida AI Research Society.2 indexed citations
10.
McCarthy, Philip M., et al.. (2009). Computational Replication of Human Paraphrase Assessment. The Florida AI Research Society. 266–271.2 indexed citations
11.
Crossley, Scott A., David F. Dufty, Philip M. McCarthy, & Danielle S. McNamara. (2007). Toward a New Readability: A Mixed Model Approach. eScholarship (California Digital Library). 29(29).35 indexed citations
12.
Dempsey, Kyle B., Philip M. McCarthy, & Danielle S. McNamara. (2007). Using phrasal verbs as an index to distinguish text genres. The Florida AI Research Society. 217–222.14 indexed citations
13.
Rus, Vasile, Philip M. McCarthy, Arthur C. Graesser, Mihai Lintean, & Danielle S. McNamara. (2007). Assessing Student Self-Explanations in an Intelligent Tutoring System. eScholarship (California Digital Library). 29(29).3 indexed citations
14.
Crossley, Scott A., Philip M. McCarthy, & Danielle S. McNamara. (2007). Discriminating between second language learning text-types. The Florida AI Research Society. 205–210.11 indexed citations
15.
McCarthy, Philip M., et al.. (2007). Using Computational Text Analysis Tools to Compare the Lyrics of Suicidal and Non-Suicidal Songwriters. eScholarship (California Digital Library). 29(29).15 indexed citations
16.
McCarthy, Philip M., et al.. (2006). Automating Text Propositionalization: An Assessment of AutoProp. eScholarship (California Digital Library). 28(28).1 indexed citations
17.
Duran, Nicholas D., Arthur C. Graesser, Philip M. McCarthy, & Danielle S. McNamara. (2006). Using Coh-Metrix Temporal Indices to Predict Psychological Measures of Time. eScholarship (California Digital Library). 28(28).8 indexed citations
18.
Dempsey, Kyle B., Philip M. McCarthy, & Danielle S. McNamara. (2006). Identifying Text Genres Using Phrasal Verbs. eScholarship (California Digital Library). 28(28).2 indexed citations
19.
Hall, Charles, et al.. (2006). Language in Law: Using Coh-Metrix to Assess Differences between American and English/Welsh Language Varieties. eScholarship (California Digital Library). 28(28).
20.
Cai, Zhiqiang, David F. Dufty, Arthur C. Graesser, et al.. (2005). Using LSA to Automatically Identify Givenness and Newness of Noun Phrases in Written Discourse. eScholarship (California Digital Library). 27(27).23 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.