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Journal ratings as predictors of articles quality in Arts, Humanities and Social Sciences: an analysis based on the Italian Research Evaluation Exercise

The aim of this paper is to understand whether the probability of receiving positive peer reviews is influenced by having published in an independently assessed, high-ranking journal: we eventually interpret a positive relationship among peer evaluation and journal ranking as evidence that journal r...

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Detalles Bibliográficos
Autores principales: Bonaccorsi, Andrea, Cicero, Tindaro, Ferrara, Antonio, Malgarini, Marco
Formato: Online Artículo Texto
Lenguaje:English
Publicado: F1000Research 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4536613/
https://www.ncbi.nlm.nih.gov/pubmed/26309731
http://dx.doi.org/10.12688/f1000research.6478.1
Descripción
Sumario:The aim of this paper is to understand whether the probability of receiving positive peer reviews is influenced by having published in an independently assessed, high-ranking journal: we eventually interpret a positive relationship among peer evaluation and journal ranking as evidence that journal ratings are good predictors of article quality. The analysis is based on a large dataset of over 11,500 research articles published in Italy in the period 2004-2010 in the areas of architecture, arts and humanities, history and philosophy, law, sociology and political sciences. These articles received a score by a large number of externally appointed referees in the context of the Italian research assessment exercise (VQR); similarly, journal scores were assigned in a panel-based independent assessment, which involved all academic journals in which Italian scholars have published, carried out under a different procedure. The score of an article is compared with that of the journal it is published in: more specifically, we first estimate an ordered probit model, assessing the probability for a paper of receiving a higher score, the higher the score of the journal; in a second step, we concentrate on the top papers, evaluating the probability of a paper receiving an excellent score having been published in a top-rated journal. In doing so, we control for a number of characteristics of the paper and its author, including the language of publication, the scientific field and its size, the age of the author and the academic status. We add to the literature on journal classification by providing for the first time a large scale test of the robustness of expert-based classification.