Modern social choice theory has spurred considerable recent work in computational rank aggregation, the problem of aggregating rankings into a consensus ranking. However, in the social choice context where rankings represent preferences of agents, or “voters,” over outcomes, or “candidates,” there is scant justification in the literature for producing rankings as an output of the aggregation pr...

In ordinal ranking problems objects, alternatives, products, services, etc. are ranked by several experts and the goal is to convert a set of (generally different) rankings into the final group consensus ranking. However, this goal depends on a degree of agreement among rankings. With random rankings one cannot expect to get meaningful consensus, but if rankings are „close“ and represent agreem...

The standard data that we use when computing bibliometric rankings of scientists are just their publication/citation records, i.e., so many papers with 0 citation, so many with 1 citation, so many with 2 citations, etc. The standard data for bibliometric rankings of departments have the same structure. It is therefore tempting (and many authors gave in to temptation) to use the same method for ...

Since their emergence a decade ago, global university rankings have become a powerful force in higher education internationally. The majority of research studies on global rankings have examined the effects at institutional and national level. This study offers a valuable perspective on ways rankings (and other international benchmarks) are deployed at the intra-sector level, by UK HE ‘mission ...

The phenomenon of rankings is intimately related with the government interest in fiscalizing the research outputs of universities. New forms of managerialism have been introduced into the higher education system, leading to an increasing interest from funding bodies in developing external evaluation tools to allocate funds. Rankings rely heavily on bibliometric indicators. But bibliometricians ...

Rankings and partial rankings are ubiquitous in data analysis, yet there is relatively little work in the classification community that uses the typical properties of rankings. We review the broader literature that we are aware of, and identify a common building block for both prediction of rankings and clustering of rankings, which is also valid for partial rankings. This building block is the...