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.
Identification of myelin-associated glycoprotein as a major myelin-derived inhibitor of neurite growth
1994931 citationsLisa McKerracher, Samuel David et al.Neuronprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of David Jackson'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 David Jackson with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Jackson more than expected).
This network shows the impact of papers produced by David Jackson. 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 David Jackson. The network helps show where David Jackson may publish in the future.
Co-authorship network of co-authors of David Jackson
This figure shows the co-authorship network connecting the top 25 collaborators of David Jackson.
A scholar is included among the top collaborators of David Jackson 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 David Jackson. David Jackson is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Amundsen, Inge & David Jackson. (2021). Rethinking anti-corruption in de-democratising regimes.1 indexed citations
3.
Jackson, David. (2020). How change happens in anti-corruption. A map of policy perspectives.3 indexed citations
4.
Jackson, David, et al.. (2020). Corruption in the time of COVID-19: A double-threat for low income countries.17 indexed citations
5.
Walton, Grant W. & David Jackson. (2020). Reciprocity networks, service delivery, and corruption: The wantok system in Papua New Guinea. ANU Open Research (Australian National University).2 indexed citations
6.
Jackson, David, et al.. (2019). Capacity building for politicians in contexts of systemic corruption: Countering ‘wasta’ in Jordan.6 indexed citations
7.
Jackson, David & Nils Köbis. (2018). Anti-corruption through a social norms lens. UvA-DARE (University of Amsterdam). 2018(7).16 indexed citations
8.
Wands, Jack R., Risa B. Mann, David Jackson, & Tom Butler. (2015). Fatal Community-Acquired Herellea Pneumonia in Chronic Renal Disease. American Review of Respiratory Disease.
9.
Jackson, David & Fang Fang. (2014). CEO Networks and Bank Risk Taking. ScholarWorks @ UTRGV (The University of Texas Rio Grande Valley). 6. 38–54.2 indexed citations
Jackson, David, et al.. (2012). Nordic art the modern breakthrough 1860-1920.1 indexed citations
12.
Jackson, David, et al.. (2011). The Impact of TARP Bailouts on Stock Market Volatility and Investor Fear. ScholarWorks @ UTRGV (The University of Texas Rio Grande Valley). 3(1). 45–54.13 indexed citations
McKerracher, Lisa, et al.. (1994). Identification of myelin-associated glycoprotein as a major myelin-derived inhibitor of neurite growth. Neuron. 13(4). 805–811.931 indexed citations breakdown →
Jackson, David. (1982). Continuity in secondary English. Medical Entomology and Zoology.4 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.