ArticlePeerJ. Computer science2019
Predicting the results of evaluation procedures of academics.
Article in PeerJ. Computer science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
3 citing papers in PubMed.
- Anatomy of climate change research in Italian doctoral dissertations using a machine learning approach.Scientific reports · 2025Article
- Using altmetrics for detecting impactful research in quasi-zero-day time-windows: the case of COVID-19.Scientometrics · 2021Article
- Predicting the results of evaluation procedures of academics.PeerJ. Computer science · 2019Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe 2010 reform of the Italian university system introduced the National Scientific Habilitation (ASN) as a requirement for applying to permanent professor positions. Since the CVs of the 59,149 candidates and the results of their assessments have been made publicly available, the ASN constitutes an opportunity to perform analyses about a nation-wide evaluation process.
objectiveThe main goals of this paper are: (i) predicting the ASN results using the information contained in the candidates' CVs; (ii) identifying a small set of quantitative indicators that can be used to perform accurate predictions. APPROACH: Semantic technologies are used to extract, systematize and enrich the information contained in the applicants' CVs, and machine learning methods are used to predict the ASN results and to identify a subset of relevant predictors.
resultsFor predicting the success in the role of associate professor, our best models using all and the top 15 predictors make accurate predictions (F-measure values higher than 0.6) in 88% and 88.6% of the cases, respectively. Similar results have been achieved for the role of full professor. EVALUATION: The proposed approach outperforms the other models developed to predict the results of researchers' evaluation procedures.
conclusionsSuch results allow the development of an automated system for supporting both candidates and committees in the future ASN sessions and other scholars' evaluation procedures.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.