Percentile-Based Citation Impact (PPtop10%)
Percentile-based citation impact replaces the average citation count with a paper's rank position within a properly defined reference set. Instead of asking how many citations a paper received, it asks where the paper falls in the citation distribution of comparable papers from the same field, year, and document type. Because citation distributions are extremely skewed, a single highly cited paper can inflate a mean, so Lutz Bornmann and Loet Leydesdorff argued that impact should be measured non-parametrically through percentile ranks and the share of papers reaching the top of their field. The most widely used summary is PPtop10%, the proportion of a unit's papers that belong to the most-cited 10% of their reference set; Leydesdorff and Bornmann's Integrated Impact Indicator (I3) generalizes this idea by integrating the full percentile curve. Ludo Waltman and Michael Schreiber clarified how percentile ranks should be computed when many papers share the same citation count.
Source record
Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.
- Leydesdorff, L., & Bornmann, L. (2011). Integrated impact indicators compared with impact factors: An alternative research design with policy implications. Journal of the American Society for Information Science and Technology, 62(11), 2133-2146. · DOI 10.1002/asi.21609
- Waltman, L., & Schreiber, M. (2013). On the calculation of percentile-based bibliometric indicators. Journal of the American Society for Information Science and Technology, 64(2), 372-379. · DOI 10.1002/asi.22775
Curated claims
Claims persisted in the evidence ledger, each with its own assessment.
This view does not invent a claim assessment when the ledger has none.
Related methods
Generated from the method graph and shown as machine-suggested relations — no evidence claim is inferred.