Examples
Worked examples
- Is an instance
A co-author on a large physics collaboration paper: the May 2015 Physical Review Letters paper 'Combined Measurement of the Higgs Boson Mass in pp Collisions at sqrt(s)=7 and 8 TeV with the ATLAS and CMS Experiments' listed 5,154 authors, a widely reported record for a single research article (Nature News, Science/AAAS, and Physics Today all covered it at the time). Every one of those thousands of co-authors receives that paper's full citation count toward their own h-index, regardless of their specific role in the analysis.
- Is an instance
A co-author on a large genomics consortium paper: hyperauthorship -- publications with unusually large author lists -- is well documented as increasingly common in genomics and high-energy physics specifically, per comparative bibliometric studies of authorship patterns in those two fields (e.g. Cronin's and later Ioannidis/Klavans/Boyack's work on hyperauthorship and h-index reliability, summarized in a 2021 PLOS ONE analysis). A genomics researcher whose h-index rests heavily on a handful of large-consortium sequencing papers is in the same structural position as the physics co-author above.
Counter-examples
Looks similar, but isn't
- Not an instance
A researcher whose h-index is built from a broad base of papers each with a small, stable co-author list (say, two to six authors), none of which individually accounts for a disproportionate share of their total citations, is not exhibiting index inflation even if their h-index is numerically high -- the number reflects a wide pattern of individually attributable, independently cited work rather than a few very-large-team papers.
Editorial commentary
The h-index was designed by Jorge Hirsch in 2005 as a single number combining a researcher’s productivity and citation impact: a researcher has an h-index of h if h of their papers have each been cited at least h times. That design assumes, implicitly, that authorship reflects a roughly comparable level of individual contribution across papers — an assumption that breaks down on papers with hundreds or thousands of listed co-authors, a pattern known in the bibliometrics literature as hyperauthorship.
H-index inflation is what results when that assumption breaks down in a researcher’s favor: their raw h-index rises not because they have produced a broad body of independently cited work, but because they appear as one name among many on one or a small number of hyperauthored papers that attract heavy citation. Large-team papers are especially common in fields organized around shared instruments or shared datasets — high-energy physics experiments run through international collaborations like ATLAS and CMS at CERN, and genomics consortia built around shared sequencing infrastructure are the two fields most frequently cited in the literature on this pattern.
Why this matters for research assessment
Research funders, hiring and tenure committees, and research information management systems that surface h-index as a comparison metric are all exposed to the same distortion: a raw h-index treats a co-author credited on a 5,000-author paper identically to a sole author of an equally-cited paper. A peer-reviewed analysis published in PLOS ONE in 2021 (Ioannidis, Klavans, and Boyack) found that the h-index’s correlation with receiving major scientific awards — one proxy for genuine scientific standing — weakened over the 2010s in fields where hyperauthorship became more common, which the authors attribute in part to this inflationary effect. This is one of the reasons the San Francisco Declaration on Research Assessment (DORA) and the Coalition for Advancing Research Assessment (CoARA) both discourage relying on any single citation-based number, including the h-index, as a standalone proxy for research quality or individual contribution.
How bibliometricians and CRIS practitioners address it
There is no single accepted fix, but several adjustments are used in practice to reduce the effect of hyperauthorship on impact metrics: fractional counting (dividing a paper’s citation credit across its author list, sometimes called an ‘h-frac’ approach), contribution-based approaches such as the CRediT taxonomy that record what each author actually did rather than treating authorship as binary, and simply reading the underlying publication list behind a headline h-index rather than treating the number as self-explanatory. None of these are built into the standard h-index calculation offered by Scopus, Web of Science, or Google Scholar, which is why the raw figure alone cannot distinguish an inflated h-index from one built on broad, individually attributable impact.
What this page does not attempt
This page does not rank or list specific researchers by current h-index. Third-party trackers (Google Scholar profiles, Scopus author pages, and aggregator sites) publish live, frequently-changing numbers for named individuals; any such ranking is out of date within months and is not the kind of stable reference content this site maintains. The durable, evergreen fact worth documenting is the structural mechanism above — why very high h-index values can occur through team size rather than individual impact — not a snapshot leaderboard.
Related terms
- h-index — the underlying metric
- What counts as a "good" h-index
- i10-index — a related Google Scholar productivity metric
- Impact Factor vs. h-index
- h-index vs. i10-index
- Citation impact
Machine-readable encodings
Use in your systems
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