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Earlier studies describing and categorising the development of museum websites either build on theoretical knowledge or the analysis of a qualitative sample of relatively few, but prominent museum websites. The present study takes a broader approach and aims to characterise the development across Danish museum websites through the years 2005–2020. The methodological approach is a diachronic (temporal) analysis of Danish museum websites using data extracted from the Danish Web. A cluster anal-ysis was used to identify similarities between museum websites. The cluster analysis shows that the museum website representations possess distinct attributes pivotal to their degree of resemblance with others, such as collection type, geographical location, organisational structure and whether the communication and dis-semination strategy focus on disseminating collection content or extends to a broader communication scope. However, the quantitative text analysis approach to studying archived museum web-sites did not identified a new typology. Instead, the study suggests how a cluster analysis of the Danish museum web sphere can constitute an initial step in comprehending domain development, and ultimately discerning the attributes that characterise the different museum website clusters

This evidence based practice paper presents preliminary results in using an artificial intelligence classifier to mark student assignments in a large class setting. The assessment task consists of an approximately 2000 word reflective essay that is produced under examination conditions and submitted electronically. The marking is a simple pass/fail determination, and no explicit feedback beyond the pass/fail grade is provided to the students. Each year around 1500 students complete this assignment, which places a significant and time-constrained marking load upon the teaching faculty.This paper presents a Natural Language Process (NLP) framework/tool for developing a machine learning based binary classifier for automated assessment of these assignments. The classifier allocates each assignment a score representing the probability that the assignment would receive a passing grade from a human marker. The effectiveness and performance of the classifier is measured by investigating the accuracy of those predictions.Several iterations and statistical analyses were carried out to determine operational thresholds that balance the risks of false positives and false negatives with the required quantity of human marking to assess the assignment.The resulting classifier was able to provide accuracy levels that are potentially feasible in an operational context, and the potential for significant overall reductions in the human marking load for this assignment.

Computational typology has gained traction in the field of Natural Language Processing (NLP) in recent years, as evidenced by the increasing number of papers on the topic and the establishment of a Special Interest Group on the topic (SIGTYP), including the organisation of successful workshops and shared tasks.A considerable amount of work in this sub-field is concerned with prediction of typological features, e.g., for databases such as the World Atlas of Language Structures (WALS) or Grambank.Prediction is argued to be useful either because (1) it allows for obtaining feature values for relatively undocumented languages, alleviating the sparseness in WALS, in turn argued to be useful for both NLP and linguistics; and (2) it allows us to probe models to see whether or not these typological features are encapsulated in, e.g., language representations learned in diverse tasks such as language modelling or machine translation.In this paper, we present a critical stance of prediction of typological features, investigating to what extent this line of research is aligned with purported needs - both from the perspective of NLP practitioners, and perhaps more importantly, from the perspective of linguists specialised in typology and language documentation.We provide evidence that this line of research in its current state suffers from a lack of interdisciplinary alignment.Based on an extensive survey of the linguistic typology community, we present concrete recommendations for future research in order to improve this alignment between linguists and NLP researchers, beyond the scope of typological feature prediction.

Introduction : The aim is to report early clinical results of an experimental surgical procedure, injecting bone void filler, for patients with lower extremity rheumatoid arthritis (RA) insufficiency fractures. Case Report: We used a cross-sectional design with retrospective follow-up. Patient-reported outcomes were reported by the body-region specific questionnaires, knee injury and osteoarthritis outcome score (KOOS), or foot and ankle outcome score (FOAS). The Eq5D-5L questionnaire reported overall health-related quality of life (QoL). Furthermore, patient satisfaction (semi-structured interview), pain reactions, and adverse events were reported. Results from the first five patients were included. The median age was 70 years (range 50–80). Fracture localizations were proximal tibia (2), distal tibia (2), and distal femur (1). Four out of five patients reported a high degree of patient satisfaction following treatment. Four out of five patients at follow-up reported no pain from the fracture location. The median improvement from baseline to follow-up on the KOOS/FAOS subscale scores was pain 65 (range 42–69), symptoms 39 (range 25–50), the activity of daily living 41 (range 29–51), sport and recreational activities (sport/rec) 48 (range 40–60), and QoL 60 (range 57–72). The median improvement for the Eq5D index was 0.30 (range 0.26–0.36). Conclusion: Surgical treatment of insufficiency fractures in RA patients with bone void filler seems promising. Four out of five patients reported considerable improvement in function and QoL and substantially lower pain scores following treatment. More research is needed to investigate the efficacy of this novel surgical procedure. Keywords: Bone void filler, insufficiency fractures, surgical.

This article examines And Justice for All’s (1979) peculiar spin on the courtroom drama. Though the film embraces a mode of seriousness to portray sexual violence and an unjust criminal justice system, it also includes an undercurrent of dark comedy and absurdity. The article shows how the film incorporates dark-comedic absurdity to emphasize how severely malfunctional the criminal justice system is. While the film reproduces the lawyer-as-hero trope known from earlier eras in American film history, it is very disillusioned with the state of the criminal justice system as such. In this sense, it gives viewers a recognizable lawyer-hero to root for even though the film invites viewers to be very skeptical of the state of the system.

From the mid-1960s to the mid 1970s, three new universities were established in Denmark: Odense (1966), Roskilde (1972) and Aalborg (1974). Until then only two universities existed in Denmark: Copenhagen (1479) and Aarhus (1928). Located on the outskirts of the major cities, the new institutions played an important part in reforming and transforming higher education in Denmark. The youngest university, Aalborg University, is approaching its 50th anniversary. This is an opportunity to take a step back and examine the university in long-term perspective. In this article, I investigate the discussions and expectations concerning the establishment of the university in the period 1958–1974. Based on contemporaneous debates in the regional newspaper Aalborg Stiftstidende and interviews with several key figures, the study seeks to analyze the underlying ideas and ideals which characterized the discussion by asking the following questions: What was the projected purpose of the new university? What ideals were expressed in the debate? In the last part of this article, I will reflect on the historiographical developments in the history of the university. In recent years, several new approaches have expanded the field and made visible new aspects of the institutions’ histories. I will discuss why a focus on the ideas, ideals and expectations of the university, and thereby of the university as a knowledge institution, is essential to include in a study of its history.

Assessing and comprehending the social impact of firms at global and local level is a pressing concern for both researchers and policy-makers. To address this concern, our paper contributes to the stream of literature that studies the content of Corporate Social Responsibility (CSR) reports (which are also referred to as non-financial statements, sustainability reports or parts of annual reports) using text mining methods. We present a novel approach called Standard-based Impact Classification method (SBIC method), which employs natural language processing (NLP) and supervised machine learning techniques to identify the types of social impacts reflected in CSR reports. We deploy a Random Forest model which we train on reports adhering to Global Reporting Initiative (GRI) framework, enabling the identification of social impact in the majority of CSR reports that do not conform to this standard. Our proposed SBIC method serves as a valuable tool for comparing the social impacts generated by firms, industries, or countries. We showcase an application of our approach by examining the relationship between a company’s social impact and its innovation capacity. Our findings support the existing literature consensus that CSR activities generally exhibit a positive correlation with a firm’s ability to innovate. Furthermore, we reveal that specific types of social impacts have a more pronounced influence on innovation capacity.

Standardized laboratory testing is normally used for characterization of building materials and components. In real life, especially when assessing buildings in terms of waterdamaged building materials, built-in moisture, rising damp or other problems, information on the hygrothermal state of the constructions may be relevant, but is often sparse. This paper presents a study where application of a newly developed non-destructive experimental method is used to measure the relative humidity (RH) at the surface of four types of building materials. Knowing the RH at the surface, a risk assessment of fungal growth can be made. The materials include concrete, brick, aerated concrete, and gypsum, with and without a surface treatment. The method is based on the idea of establishing a water vapor transport towards equilibrium within a small air volume in contact with the surface of interest. In the laboratory the specimens were conditioned at respectively 65 % RH and 90 % RH in a climate chamber, and then transferred to conditions with 50 % RH. The open side of a Petri dish, equipped with a temperature and relative humidity sensor, was attached to the materials using reusable adhesive. The development of the relative humidity over time within the petri dish was monitored and parameterized mathematically, solving an exponential rate equation. The parameterization and certain sets of assumptions allow among other things a calculation of time to equilibrium which makes it possible to assess the reproducibility and comparison in between materials. As the materials were tested with and without surface treatment, it was possible to estimate the effect of a typical surface treatment on the moisture transport. The measurements compared well to simulations made by a model, though the model predicts slightly faster achievement of equilibrium. Despite the limited number of cases in the current study, the presented results are promising with regards to enable rough estimates of RH at the surface of building materials outside the laboratory with a very simple, inexpensive, and non-destructive method.