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.
The rise of generative artificial intelligence (AI) language models - challenges and opportunities for geographical and environmental education
202355 citationsChew‐Hung Chang, Gillian KidmanInternational Research in Geographical and Environmental Educationprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Gillian Kidman
Since
Specialization
Citations
This map shows the geographic impact of Gillian Kidman'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 Gillian Kidman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gillian Kidman more than expected).
This network shows the impact of papers produced by Gillian Kidman. 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 Gillian Kidman. The network helps show where Gillian Kidman may publish in the future.
Co-authorship network of co-authors of Gillian Kidman
This figure shows the co-authorship network connecting the top 25 collaborators of Gillian Kidman.
A scholar is included among the top collaborators of Gillian Kidman 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 Gillian Kidman. Gillian Kidman is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Chang, Chew‐Hung & Gillian Kidman. (2023). The rise of generative artificial intelligence (AI) language models - challenges and opportunities for geographical and environmental education. International Research in Geographical and Environmental Education. 32(2). 85–89.55 indexed citations breakdown →
Kidman, Gillian, Stephen Keast, & Rebecca Cooper. (2013). Enhancing preservice teacher learning through slowmation animation. QUT ePrints (Queensland University of Technology).2 indexed citations
11.
Kidman, Gillian, Stephen Keast, & Rebecca Cooper. (2012). Responding to the 5Rs: An alternate perspective of slowmation. QUT ePrints (Queensland University of Technology).6 indexed citations
12.
Kidman, Gillian, Stephen Keast, & Rebecca Cooper. (2012). Understanding pre-service teacher conceptual change through slowmation animation. QUT ePrints (Queensland University of Technology).2 indexed citations
13.
King, Donna, et al.. (2012). Connecting Indigenous Stories with Geology: Inquiry-Based Learning in a Middle Years Classroom.. QUT ePrints (Queensland University of Technology). 58(2). 41–46.5 indexed citations
14.
Kidman, Gillian & Fivos Papadimitriou. (2012). Content analysis of international research in geographical and environmental education : eighteen years of academic publishing.1 indexed citations
15.
Kidman, Gillian, et al.. (2012). Teaching and learning family and consumer sciences through K-W-L charts. QUT ePrints (Queensland University of Technology).1 indexed citations
Ritchie, Stephen M., et al.. (2007). Role identities in narratives: Continuing the story. QUT ePrints (Queensland University of Technology). 2. 259–264.2 indexed citations
19.
Kidman, Gillian, et al.. (2004). Reducing maths-anxiety: Results from an online anxiety survey. Faculty of Education.10 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.