Disciplines display field-specific ways of valuing research contributions, and these different ‘styles of valuation’ influence how academic careers are assessed and formed. Yet, differences in how research is evaluated are also prevalent between different levels of assessment: collegial and organizational. Consequently, we employ a multifaceted two-dimensional approach for studying styles of valuation where both horizontal (across domains) and vertical (organization levels) variations in assessment practices are examined. For this purpose, we make use of 16 faculty guidelines and 112 referee reports concerning candidates for becoming ‘docent’ (Habilitation) from four broad domains: the humanities, the social sciences, medicine and the natural sciences (including technology). By inductively identifying five broad dimensions used when assessing publication merits: (1) Attribution of work, (2) Qualities of content, (3) Publication channel, (4) Publication impact, and (5) Publication volume we can distinguish specific styles of valuation for each of our four domains. Moreover, by extending the analysis to an organizational level we detect opposing ways in which the evaluations are justified—what we call ‘disharmonic styles of valuation’. Thus, when developing insights on ‘quality understandings’—and their operationalization through styles of valuation—in academia we need to put less emphasis on their origins and rather focus on how they come to travel between and co-exist within specific evaluative contexts.

Traditionell vetenskaplig publicering utmanas av idén om öppen tillgång. Gustaf Nelhans skriver om risker och svårigheter och om hur forskarsamhället måste förändras för att idealet ska bli verklighet.

This study explores integrating generative AI to enhance citation context typing. Using Claude LLM, we generate synthetic data aligned with the Citation Typing Ontology (CiTO) to train a classifier. This supervised learning experiment involves training a classifier to identify citation types using this synthetic data. We evaluate the classifier’s performance on uncategorised citation statements. Additionally, we extend our analysis to test the classifier trained on English language citation context statements on statements extracted from Swedish and German research publications. A novel aspect of this work lies in the fusion of bibliometrics and experimental work in semantic modelling, employing language models to train machine learning models for research content evaluation. While acknowledging the inherent limitations of machine learning algorithms, we propose further testing using real-time scenarios and human evaluators. This study aims to push the boundaries of research methodology by integrating generative AI beyond text generation into the research process itself.

Syftet med denna artikel är att presentera en övergripande bibliometrisk utforskning och analys av konversationer inom lärarutbildningsforskning och hur denna komplexa verksamhet organiserar sig i form av forskningsproblem, kunskapsintressen och intellektuella traditioner. Analyserna behandlar de noder och nätverk som formas av länkar mellan publikationer genom konversationer i forskarsamhället. I fokus står svensk lärarutbildningsforskning och hur denna länkas till internationell forskning inom området. Undersökningarna bygger på de resurser som citeringsdatabasen Web of Science och analysverktyget VOSviewer erbjuder. Vi identifierade 23 866 publikationer totalt, varav 358 hade anknytning till Sverige. En omfattande expansion av publikationer har skett under senare år – såväl inom svensk som internationell lärarutbildningsforskning – med en påtaglig anglo-saxisk dominans inom området. Explorativa klusteranalyser utifrån länkar mellan publikationer gav olika forskningsfronter med skilda problem och ansatser som behandlades i skilda nätverk. Vi kunde också identifiera särskilda intellektuella traditioner och förgrundsfigurer i olika nätverk. Svensk och internationell forskning uppvisade här åtskilliga likheter i sin organisering. Våra studier visar på ett fragmenterat forskningsfält positionerat i olika sammanhang och kunskapsintressen. Forskarsamhället behöver reflektera över skillnader i inriktning och lokalisering av forskningsproblem inom fältet samt analysera varför dessa uppstår liksom innebörder av att lärarutbildning förflyttas mellan olika forskningsfronter och traditioner. 

This paper aims to study what type of research seems to interest the users of a social network platform and then complement the data with data from an open catalogue for research, exemplifying with Twitter and Open Alex. The basic idea is to get an overview of the stories the platform content tells during three months regarding topics, disciplines, and open access status. The findings suggest that the picture look very different between the approaches to map the topics, especially when looking at the articles most mentioned compared to the ones that are most retweeted. The study mainly highlights the methodological opportunities of combining text analysis and link relationships to explore the content and public interest in academic research.

We study terrorism’s shaping of STEM research through the development within engineering research of a ‘terrorismmindedness’, i.e. terrorist threat domestication through integration in research practice. This is done by a distant reading of how research in the engineering sciences is increasingly addressing terrorism-related topics. By means of an in-depth bibliometric analysis of some 3.000 terrorism-related scientific articles published 1989–2022, we construct within the subject area ‘Engineering’ in Web of Science its research subfield ‘Terrorism Related Engineering Research’. The publications are analysed by bibliometric mapping, co-occurrence text measures and ‘algorithmic historiography’ using the HistCite tool. Papers cited together are mapped using VOSviewer to identify concepts and the results are clustered according to topicality, revealing the various terrorism-related research interests among engineering scientists.