Valuation judgement bias has been a research topic for several years due to its proclaimed effect on valuation accuracy. However, little is known on the emphasis of literature on judgement bias, with regard to, for instance, research methodologies, research context and robustness of research evidence. A synthesis of available research will establish consistency in the current knowledge base on valuer judgement, identify future research opportunities and support decision-making policy by educational and regulatory stakeholders how to cope with judgement bias. This article therefore, provides a systematic review of empirical research on real estate valuer judgement over the last 30 years. Based on a number of inclusion and exclusion criteria, we have systematically analysed 32 relevant papers on valuation judgement bias. Although we find some consistency in evidence, we also find the underlying research to be biased; the methodology adopted is dominated by a quantitative approach; research context is skewed by timing and origination; and research evidence seems fragmented and needs replication. In order to obtain a deeper understanding of valuation judgement processes and thus extend the current knowledge base, we advocate more use of qualitative research methods and scholars to adopt an interpretative paradigm when studying judgement behaviour.
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Background: Successful implementation of multifactorial fall prevention interventions (FPIs) is essential to reduce increasing fall rates in communitydwelling older adults. However, implementation often fails due to the complex context of the community involving multiple stakeholders within and across settings, sectors, and organizations. As there is a need for a better understanding of the occurring context-related challenges, the current scoping review purposes to identify what contextual determinants (i.e., barriers and facilitators) influence the implementation of FPIs in the community. Methods: A scoping reviewwas performed using the Arksey andO’Malley framework. First, electronic databases (Pubmed, CINAHL, SPORTDiscus, PsycINFO) were searched. Studies that identified contextual determinants that influence the implementation of FPIs in the community were included. Second, to both validate the findings from the literature and identify complementary determinants, health and social care professionals were consulted during consensus meetings (CMs) in four districts in the region of Utrecht, the Netherlands. Data were analyzed following a directed qualitative content analysis approach, according to the 39 constructs of the Consolidated Framework for Implementation Research. Results: Fourteen relevant studies were included and 35 health and social care professionals (such as general practitioners, practice nurses, and physical therapists) were consulted during four CMs. Directed qualitative content analysis of the included studies yielded determinants within 35 unique constructs operating as barriers and/or facilitators. The majority of the constructs (n = 21) were identified in both the studies and CMs, such as “networks and communications”, “formally appointed internal implementation leaders”, “available resources” and “patient needs and resources”. The other constructs (n = 14) were identified only in the . Discussion: Findings in this review show that awide array of contextual determinants are essential in achieving successful implementation of FPIs in the community. However, some determinants are considered important to address, regardless of the context where the implementation occurs. Such as accounting for time constraints and financial limitations, and considering the needs of older adults. Also, broad cross-sector collaboration and coordination are required in multifactorial FPIs. Additional context analysis is always an essential part of implementation efforts, as contexts may differ greatly, requiring a locally tailored approach.
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A large body of research has described the influence of context information on forensic decision-making. In this study, we examined the effect of context information on the search for and selection of traces by students (N = 36) and crime scene investigators (N = 58). Participants investigated an ambiguous mock crime scene and received prior information indicating suicide, a violent death or no information. Participants described their impression of the scene and wrote down which traces they wanted to secure. Results showed that con- text information impacted first impression of the scene and crime scene behavior, namely number of traces secured. Participants in the murder condition secured most traces. Furthermore, the students secured more crime-related traces. Students were more confident in their first impres- sion. This study does not indicate that experts outperform novices. We therefore argue for proper training on cognitive processes as an integral part of all forensic education.
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With the proliferation of misinformation on the web, automatic misinformation detection methods are becoming an increasingly important subject of study. Large language models have produced the best results among content-based methods, which rely on the text of the article rather than the metadata or network features. However, finetuning such a model requires significant training data, which has led to the automatic creation of large-scale misinformation detection datasets. In these datasets, articles are not labelled directly. Rather, each news site is labelled for reliability by an established fact-checking organisation and every article is subsequently assigned the corresponding label based on the reliability score of the news source in question. A recent paper has explored the biases present in one such dataset, NELA-GT-2018, and shown that the models are at least partly learning the stylistic and other features of different news sources rather than the features of unreliable news. We confirm a part of their findings. Apart from studying the characteristics and potential biases of the datasets, we also find it important to examine in what way the model architecture influences the results. We therefore explore which text features or combinations of features are learned by models based on contextual word embeddings as opposed to basic bag-of-words models. To elucidate this, we perform extensive error analysis aided by the SHAP post-hoc explanation technique on a debiased portion of the dataset. We validate the explanation technique on our inherently interpretable baseline model.
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People tend to be hesitant toward algorithmic tools, and this aversion potentially affects how innovations in artificial intelligence (AI) are effectively implemented. Explanatory mechanisms for aversion are based on individual or structural issues but often lack reflection on real-world contexts. Our study addresses this gap through a mixed-method approach, analyzing seven cases of AI deployment and their public reception on social media and in news articles. Using the Contextual Integrity framework, we argue that most often it is not the AI technology that is perceived as problematic, but that processes related to transparency, consent, and lack of influence by individuals raise aversion. Future research into aversion should acknowledge that technologies cannot be extricated from their contexts if they aim to understand public perceptions of AI innovation.
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Although empathy is an essential aspect of co-design, the design community lacks a systematic overview of the key dimensions and elements that foster empathy in design. This paper introduces an empathic formation compass, based on a comparison of existing relevant frameworks. Empathic formation is defined here as the formative process of becoming an empathic design professional who knows which attitude, skills and knowledge are applicable in a co-design process. The empathic formation compass provides designers with a vocabulary that helps them understand what kind of key dimensions and elements influence empathic formation in co-design and how that informs designers’ role and design decisions. In addition, the empathic formation compass aims to support reflection and to evaluate co-design projects beyond the mere reliance on methods. In this way, empathic design can be made into a conscious activity in which designers regulate and include their own feelings and experiences (first-person perspective), and decrease empathic bias. We identify four important intersecting dimensions that empathy is comprised of in design and describe their dynamic relations. The first two opposing dimensions are denoted by empathy and differentiate between cognitive design processes and affective design experiences, and between self-and other orientation. The other two dimensions are defined by design research and differentiate between an expert and a participatory mindset, and research-and design-led techniques. The empathic formation compass strengthens and enriches our earlier work on mixed perspectives with these specific dimensions and describes the factors that foster empathy in design from a more contextual position. We expect the empathic formation compass—combined with the mixed perspectives framework—to enhance future research by bringing about a deeper understanding of designers’ empathic and collaborative design practice.
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This study aims to map VE teachers’ perceived importance of assessment competence. The study was conducted in the Netherlands among teachers of professional studies in Universities of Applied Sciences. A large-scale study was conducted to represent a broad population of teachers, including various vocational fields, roles, and situations, allowing for the exploration of differences across these contextual variables.
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L1 grammar teaching worldwide often takes the form of traditional grammar teaching with decontextualized parsing exercises and rules of thumb. Some researchers have proposed enriching such forms of grammar teaching by relating traditional grammatical concepts to underlying metaconcepts from linguistic theory. The merits of such an approach have become apparent in recent intervention studies, but the question remains how teachers perceive such forms of grammar teaching, which is of particular importance for curriculum development. The present study investigated Dutch teachers’ beliefs in focus groups and a national survey (N = 127). It is found that Dutch language teachers see important benefits of a metaconceptual approach to grammar teaching, particularly as a means to improve students’ grammatical understanding. However, results also indicate that while teachers may see clear pedagogical and conceptual advantages of working based on underlying metaconcepts, their own teaching practice appears to be much more traditional. This discrepancy is explained by assuming that contextual factors have a restraining effect on what teachers can or want to do in reality. Once such contextual factors no longer play a part, teachers’ views tend to be much more geared towards a metaconceptual approach. The paper concludes with some implications for future research.
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This article reflects on implications of presenting nature as a social construction, and of commodification of nature. The social construction of nature tends to limit significance of nature to human perception of it. Commodification presents nature in strict instrumental terms as ‘natural resources’, ‘natural capital’ or ‘ecosystem services’. Both construction and commodification exhibit anthropocentric bias in denying intrinsic value of non-human species. This article will highlight the importance of a deep ecology perspective, by elaborating upon the ethical context in which construction and commodification of nature occur. Finally, this article will discuss the implications of this ethical context in relation to environmental education (EE) and education for sustainable development (ESD). https://doi.org/10.3384/cu.2000.1525.146931 https://www.linkedin.com/in/helenkopnina/
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In the literature about web survey methodology, significant e orts have been made to understand the role of time-invariant factors (e.g. gender, education and marital status) in (non-)response mechanisms. Time-invariant factors alone, however, cannot account for most variations in (non-)responses, especially fluctuations of response rates over time. This observation inspires us to investigate the counterpart of time-invariant factors, namely time-varying factors and the potential role they play in web survey (non-)response. Specifically, we study the effects of time, weather and societal trends (derived from Google Trends data) on the daily (non-)response patterns of the 2016 and 2017 Dutch Health Surveys. Using discrete-time survival analysis, we find, among others, that weekends, holidays, pleasant weather, disease outbreaks and terrorism salience are associated with fewer responses. Furthermore, we show that using these variables alone achieves satisfactory prediction accuracy of both daily and cumulative response rates when the trained model is applied to future unseen data. This approach has the further benefit of requiring only non-personal contextual information and thus involving no privacy issues. We discuss the implications of the study for survey research and data collection.
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