Citation impacts revisited: How novel impact measures reflect interdisciplinarity and structural change at the local and global level

Citation networks have fed numerous works in scientific evaluation, science mapping (and morerecently large-scale network studies) for decades. The variety of citation behavior across scientificfields is both a research topic in sociology of science, and a problem in scientific evaluation.Normalization, tantamount to a particular weighting of links in the citation network, is necessaryfor allowing across-field comparisons of citation scores and interdisciplinary studies. In additionto classical normalization which drastically reduces all variability factors altogether, two tracks ofresearch have emerged in the recent years. One is the revival of iterative “influence measures”. Thesecond is the “citing-side” normalization, whose only purpose is to control for the main factor ofvariability, the inequality in citing propensity, letting other aspects play: knowledge export/imports and growth. When all variables are defined at the same field-level, two propositions areestablished: (a) the gross impact measure identifies with the product of relative growth rate, grossbalance of citation exchanges, and relative number of references (b) the normalized impact identifieswith the product of relative growth rate and normalized balance. At the science level, thevariance of growth rate over domains is a proxy for change in the system, and the variance ofbalance a measure of inter-disciplinary dependences. This opens a new perspective, where theresulting variance of normalized impact, and a related measure, the sum of these variancesproposed as a Change-Exchange Indicator, summarize important aspects of science structure anddynamism. Results based on a decade’s data are discussed. The behavior of normalized impactaccording to scale changes is also briefly discussed. A shift towards a network-based definition ofdomains, more in the nomenclature-free spirit of citing-side normalization than database classificationschemes, appears promising, albeit with technical challenges. An appealing issue is theconnection with macro-level life-cycles of domains, and the dynamics of citation network