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Artificial Intelligence without BIAS (NoBIAS)


Leader
Prof. Dr. Claudia Wagner
Katharina Kinder-Kurlanda

Team
Antonio Ferrara

Abstract

Very recently it was proposed that we report a self-citation index s rather than rely on curated scorekeeping. Here, a scholar has an index s if he or she has published s articles each of which has received at least s self-citations.

Defining s similar to h makes it easy for people to understand, and importantly, pairing h and s indices, unlike excluding, appreciates the worth of self-cites, while at the same time it deters excessive tendencies. Reporting, unlike excluding, will allow us to see clearly how the different academic disciplines are resorting to self-citing, thereby making excessive behavior more identifiable, explainable, and accountable. Transparency in this context is not punitive but revelatory. Showing an s-index should push academic institutions and selection committees to think more carefully and less rigidly about the h-index.

A necessary step towards using the metric is validation. Here we propose to use the s-index to measure how self-citation patterns vary according to different fields, academic ages, countries, and institutions.

In this project we want to apply  the self-citation index to a large fraction of Web of Science data in the KB infrastructure. The project proposes the following work packages:

  1. Identification of self-citations in WoS data
  2. Implementation of a self-citation index in the KB infrastructure
  3. Systematic analysis of self-citations of different disciplines and journals within WoS (WoS subject area)
  4. Systematic analysis of self-citations of a random sample of researchers in different career stages

Runtime
01.01.2020 – 31.12.2023

Sponsored by

Horizon 2020