Impact Factors and the Central Limit Theorem: Why Citation Averages Are Scale Dependent
Manolis Antonoyiannakis

TL;DR
This paper uses the Central Limit Theorem to explain the scale dependence of Impact Factors, revealing biases in journal rankings based on size and proposing a size-adjusted index for fairer comparisons.
Contribution
It introduces the $\Phi$ index, a size-adjusted Impact Factor, and demonstrates how CLT explains the scale-dependent behavior of citation averages in journal rankings.
Findings
Impact Factors fluctuate around the population mean with a range proportional to 1/√n.
Smaller journals tend to have more variable IFs, affecting their ranking.
The proposed $\Phi$ index corrects for size effects, enabling fairer comparisons.
Abstract
Citation averages, and Impact Factors (IFs) in particular, are sensitive to sample size. We apply the Central Limit Theorem (CLT) to IFs to understand their scale-dependent behavior. For a journal of randomly selected papers from a population of all papers, we expect from the CLT that its IF fluctuates around the population average , and spans a range of values proportional to , where is the variance of the population's citation distribution. The dependence has profound implications for IF rankings: The larger a journal, the narrower the range around where its IF lies. IF rankings therefore allocate an unfair advantage to smaller journals in the high IF ranks, and to larger journals in the low IF ranks. We expect a scale-dependent stratification of journals in IF rankings, whereby small journals occupy top, middle, and bottom…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
Topicsscientometrics and bibliometrics research · Complex Network Analysis Techniques · Complex Systems and Time Series Analysis
