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Health policy and academic discourses on rare diseases and people with rare conditions frequently employ terms such as 'low prevalence' and 'unique' to characterize the smallness of the population under consideration and to justify targeted action toward these patient groups. This paper draws from recent anthropological scholarship on smallness and data, ethnographic research in Finland and Poland, as well as document and media analysis to examine how data is utilized in the context of isolated populations that are considered sites of rare diseases in these two countries. Specifically, this paper juxtaposes the notion of Finnish Disease Heritage (FDH) with that of a 'Kashubian gene' in Poland. The concept of FDH was developed by Finnish researchers in the 1970s; it encompasses almost forty rare hereditary diseases that are significantly more prevalent in Finland than elsewhere globally. On the other hand, the notion of the 'Kashubian gene' was first utilized by the media and some members of the Polish medical community around 2008. Based on 'unstable' data gathered during genetic research, the term referred to the high prevalence of a rare metabolic disorder (Long-Chain 3-Hydroxyacyl-CoA Dehydrogenase (LCHAD) deficiency) among Kashubians, an ethnic minority that resides in Northern Poland's Pomerania region. Whereas FDH facilitated the production and branding of 'a unique Finnish genetic identity' (Tupasela 2016b, 61), the notion of the 'Kashubian gene' has engendered health policy interventions targeting members of this ethnic minority and has contributed to stigmatizing practices carried out against Kashubians. [ABSTRACT FROM AUTHOR]

The number of people living in cities is continuously growing, and the buildings in topographic maps are in need of frequent updates, which are costly to perform manually. This makes automatic building extraction a significant research subject. Transfer learning, on the other hand, offers solutions in situations where the data of a target area are scarce, making it a profitable research subject. Moreover, in previous studies, there was a lack of metrics in quantifying the accuracy improvement with transfer learning techniques. This paper investigated various transfer learning techniques and their combinations with U-Net for the semantic segmentation of buildings from true orthophotos. The results were analyzed using quantitative methods. Open-source remote sensing data from Poland were used for pretraining a model for building segmentation. The fine-tuning techniques including a fine-tuning contracting path, a fine-tuning expanding path, a retraining contracting path, and a retraining expanding path were studied. These fine-tuning techniques and their combinations were tested with three local datasets from the diverse environment in Finland: urban, suburban, and rural areas. Knowledge from the pretrained model was transferred to the local datasets from Helsinki (urban), Kajaani (suburban), and selected areas in Finland (rural area). Three models with no transfer learning were trained from scratch with three sets of local data to compare the fine-tuning results. Our experiment focused on how various transfer learning techniques perform on datasets from different environments (urban, suburban, and rural areas) and multiple locations (southern, northern, and across Finland). A quantitative assessment of performance improvement by using transfer learning techniques was conducted. Despite the differences in datasets, the results showed that using transfer learning techniques could achieve at least 5% better accuracy than a model trained from scratch with several different transfer learning techniques. In addition, the effect of the sizes of training datasets was also studied. [ABSTRACT FROM AUTHOR]

This paper investigates different uses of the Journal Impact Factor (JIF) in national journal rankings and discusses the merits of supplementing metrics with expert assessment. Our focus is national journal rankings used as evidence to support decisions about the distribution of institutional funding or career advancement. The seven countries under comparison are China, Denmark, Finland, Italy, Norway, Poland, and Turkey—and the region of Flanders in Belgium. With the exception of Italy, top‐tier journals used in national rankings include those classified at the highest level, or according to tier, or points implemented. A total of 3,565 (75.8%) out of 4,701 unique top‐tier journals were identified as having a JIF, with 55.7% belonging to the first Journal Impact Factor quartile. Journal rankings in China, Flanders, Poland, and Turkey classify journals with a JIF as being top‐tier, but only when they are in the first quartile of the Average Journal Impact Factor Percentile. Journal rankings that result from expert assessment in Denmark, Finland, and Norway regularly classify journals as top‐tier outside the first quartile, particularly in the social sciences and humanities. We conclude that experts, when tasked with metric‐informed journal rankings, take into account quality dimensions that are not covered by JIFs. [ABSTRACT FROM AUTHOR]

Science-informed, reductionist policy has systematically failed to address wicked situations. Such situations are highly interconnected and unpredictable. As a consequence, the implementation of so-called desirable interventions can lead to the export of vulnerabilities within and across different societal domains, sectors, intersections and scales. Systemic practice is an emerging field, and highlights the need to enrich scientific inquiry and policy actions through action learning with an "extended peer community'' as a means to navigate wicked situations. In this paper, we report on the potential of game co-design as a systemic practice to improve the situation of Baltic Sea nutrient enrichment. Findings from water catchments in Finland, Sweden and Poland suggest that the co-design of serious games can both enhance the comprehension of wicked situations, and foster self-organized concerted action without imposing a convergence of perspectives amongst diverse stakeholders. [ABSTRACT FROM AUTHOR]