h-based<i>I3</i>-type multivariate vectors: multidimensional indicators of publication and citation scores is a research paper published in COLLNET Journal of Scientometrics and Information Management (2017). On theSindex it has a DataRank of 0.241. It has been cited 4 times.
Citations and publications cannot be compared without a model. We combine the Integrated Impact Indicator (I3) and the h-index into a single framework for multivariate measurement. We propose that the h-based I3-type publication and citation vectors can be used as multi-dimensional indicators in academic evaluations. Whereas I3 is based on the transformation of the citation distribution into quantiles, the h-index allows for defining three rank classes of both publications and citations, respectively: the publication vector X and the citation vector Y. The I3-type publication score I3X and the citation score I3Y can be used as single numbers to integrate and represent the publication vector X and citation vector Y. X and Y provide multivariate vectors, and I3X and I3Y integrate the multidimensional information. X = (X1, X2, X3) and Y = (Y1, Y2, Y3) (and I3X = X1 + X2 + X3 and I3Y = Y1 + Y2 + Y3, respectively) are practically applied to process the 3-dimensional distributions of academic information using the journals in library and information science as empirical examples.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.241
From this paper's citation signal
Citation Network Contribution
0
Citation network not refreshed for this result
This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.
Learn more about DataRank methodology →DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 100% comes from its base citations and 0% from the citation network.
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.