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Why do politicians not act upon citizens' deliberations? Evidence from Iceland
Get PDFPoliticians are expressing increasing support for deliberative practices around the world. However, knowledge about their actions beyond expressing support is scarce. To address this gap in the literature, this article aims to explain why politicians do not pick up the results arising from deliberative practices and integrate them into their policies. Our analysis focuses on the 2019 deliberation in Iceland as the most likely case in which we would expect such a process to occur. We use original data from 25 semi-structured interviews conducted in 2021 with Icelandic MPs elected at the national level, which also cover all the party leaders of the eight parliamentary parties in the 2017–2021 term in office. The reflexive thematic analysis finds that, irrespective of their ideological affiliation, politicians are critical of deliberative practices both in procedural and substantive terms. They display a strong belief that political representation achieved through elections must be the rule of the democratic game. As such, deliberation is considered redundant since citizens already have many ways to participate in representative democracy
Clanship, Faith and Jacobitism: a Scottish Gaelic poetry collection, 1688-93
No full textThis chapter considers a manuscript song collection compiled by Donnchadh MacRath, a Gael from northern Scotland, 1688-93. Some songs were composed by MacRath with the later pieces having the feel of unfinished drafts. Themes such as family, clan, religion and politics predominate. The second of the two volumes seems ordered chronologically, giving a running commentary on developments from the standpoint of a Jacobite clansman as the Revolution unfolded. Consternation at James VII’s flight is followed by exultation after Killiecrankie and growing dissatisfaction with the Williamite regime. The idiosyncratic use of English orthography for the writing of Gaelic has resulted in this being a relatively under-utilised source. The chapter considers the reasons for these orthographic choices and explores the way in which this unique seventeenth century source offers a Gaelic perspective on local rivalries as well as on contemporary national and international developments
Introduction to Translation in the Performing Arts
No full textThe introduction of this collection establishes the foundational themes that connect the diverse chapters exploring the relationship between translation and the Performing Arts. It outlines the book’s structure, purpose, and overarching vision, raising critical questions about the nature and implications of translational and performative exchanges in a range of Performing Arts. By proposing new paradigms and models for understanding these encounters, the book aims to advance current scholarship and redefine how we think translation in contexts of performance. The volume highlights, for the first time, the transformative intersection between translation and the Performing Arts, exploring how materiality, media, and embodiment influence these processes. In so doing, it intends to set the stage for a discussion of the nature of translation in our material world and how we engage and interact with it through different modes of performance, positioning the collection as a pioneering exploration of translation beyond text and considering the contribution that the Performing Arts can bring to contemporary translation theory
How to Categorize Collaboration During a Collaborative Puzzle-solving Task? Validation of Collaboration Profiles Using Multimodal Data in Virtual Reality Context
Get PDFIn high-stakes collaborative situations, a decline in collaboration quality can lead to adverse events with significant consequences. Analyses performed by Human factor (HF) specialists, while effective in identifying
and addressing collaboration issues, are case-specific and most of the time performed a posteriori. To address these limitations, our research focuses on a real-time assessment of collaboration processes using multimodal signals collected and analyzed during the activity. Existing collaboration profiles taxonomies face limitations such as a posteriori profiles detection and the absence of quantitative behavioral indicators that can be measured during the activity. Leveraging Virtual Reality (VR), we have developed a framework for evaluating collaboration in controlled setting, testing the effectiveness of a subset of multimodal signals to detect collaboration profiles. We test our approach in a study including 11 stereotyped collaborative scenarios applied to a VR puzzle-solving task. This study reveals the effectiveness of our approach in distinguishing between non-collaborative and highly collaborative profiles. However, challenges arise in discriminating between closely related collaborative profiles. This paper also proposes some guidelines on how to improve the collaboration profile detection framework and address other collaborative situations
Self-supervised Instance Segmentation of Diabetic Foot Ulcers via Feature Correspondence Distillation
Get PDFDiabetic foot ulcers (DFUs) are a serious complication of diabetes that can often lead to infection, amputation, and even death if not properly managed. Accurate segmentation of DFUs in medical images is crucial for effective treatment planning. In the DFUC2024 challenge, which emphasizes self-supervised learning techniques for DFU segmentation, we investigate two approaches. The first approach utilizes a DINO (self-distillation with no labels) model combined with a trainable clustering probe to map unsupervised features into discrete segmentation labels. The second approach involves modifying the STEGO model, specifically designed to distill unsupervised features into meaningful segmentation labels, by integrating self-attention features from the ViT backbone to enhance spatial information. To further improve segmentation accuracy, we propose a coarse-to-fine instance prediction framework, where initial coarse predictions are refined through focused reprocessing of detected ulcer regions. After optimizing the hyperparameters for the DFU dataset, the modified STEGO model achieves a Dice coefficient of 0.4362 and Jaccard coefficient of 0.3358. Although the proposed approach yields competitive results, the challenge of self-supervision in DFU segmentation remains significant
Stroke recovery – what are people talking about on Twitter? A content analysis
Get PDFPurpose:
Prioritisation exercises seek out what matters to key stakeholders to inform the planning of research. Social media platforms are potentially useful data sources. The aim was to examine the content of tweets, short messages containing text and pictures, to ascertain the priorities of Twitter users regarding stroke recovery.
Materials and methods:
Content analysis of Twitter was conducted. An electronic search used the identifiers: #strokesurvivor and #strokerecovery. Tweets spanning four weeks from January 2021 were analysed.
Results:
There were 1361 tweets extracted and 486 analysed following exclusion of duplicates and unrelated material. Six themes were uncovered (n = number of tweets): maintaining motivation and positivity (153); sharing of resources (146); raising awareness of stroke (74); symptomatic aspects of recovery (39); experience of rehabilitation (63); and concerns about Covid-19 (17).
Conclusions:
Despite the brevity of tweets, a rich picture arose. A key limitation was lack of biographical data about Twitter users. Recommendations about topics requiring attention from stroke researchers, clinicians and policy makers are: management of psychological problems; public perception of stroke; rehabilitation considerations including treatment burden, person-centred care and equality of care; symptom management including fatigue and aphasia. Findings can be used to supplement and validate other priority-setting exercises
A spatial autoregressive random forest algorithm for small-area spatial prediction
Get PDFIn spatial areal unit data with missing or suppressed values, it is desirable to create models that are able to predict observations that are not available. Typically, statistical spatial smoothing models fitted in a Bayesian hierarchical framework are used for this purpose, which capture any unexplained residual spatial autocorrelation in the data through conditional autoregressive (CAR) or spatial autoregressive (SAR) priors applied to a set of random effects. In contrast, typical machine learning approaches, such as random forests or neural networks, ignore this residual autocorrelation and instead base predictions on complex nonlinear feature-target relationships. In this paper we propose
SPAR
-
Forest
, a novel spatial prediction algorithm that fuses random forests with spatial smoothing models. By iteratively refitting a random forest combined with a Bayesian CAR or SAR model in one algorithm, SPAR-Forest can incorporate flexible feature-target relationships while still accounting for the residual spatial autocorrelation. Our results, based on a Scottish property price data set and multiple simulated data sets, show that SPAR-Forest outperforms Bayesian CAR/SAR models, random forests, and state-of-the-art hybrid approaches, including geographical random forests, providing a state-of-the-art framework for small-area spatial prediction
The effect of downstream translocation on Atlantic salmon Salmo salar smolt outmigration success
Get PDFTrap and transport, the capture and subsequent translocation of fish during the freshwater phase of their migration, is becoming more common as a management intervention. Although the technique can be successful, it is costly and can have unintended effects on the fish being transported. This study investigates whether trap and transport can be used to increase the migration success of Atlantic salmon, Salmo salar, smolts in naturally flowing rivers. Seaward-migrating S. salar (n = 294) from two UK rivers were tracked using acoustic telemetric techniques. Outmigration success and timing were compared between non-transported (released at the original in-river capture site) and transported (released ca. 23 km downstream of the capture site) individuals. Downstream translocation increased the proportion of fish that successfully migrated to marine waters, and there was no indication that transport reduced post-release survival. The post-release migration speed of transported fish was slower than expected but this was likely a function of their advanced migration timing rather than an inhibition of their capacity to migrate. These results suggest that trap and transport can increase the outmigration success of S. salar smolts, but the earlier river exit dates of transported fish could negatively affect their survival at sea
Wearable accelerometer-derived measures of physical activity in heart failure: insights from the DETERMINE trials
Get PDFIntroduction:
Wearable accelerometers allow continuous assessment of physical activity during normal living conditions and may be useful in evaluating the effects of treatment for heart failure. We explored the relationships between accelerometer measures of physical activity and 6-minute walk distance and patient-reported measures of functional limitation in participants across the entire left ventricular ejection fraction spectrum in the DETERMINE trials.
Methods:
A subgroup of patients in the DETERMINE trials wore a waist-based accelerometer during 7-day periods at 3 points during the trial; between screening and randomization, and weeks 8 and 14. Patients completed the Kansas City Cardiomyopathy Questionnaire (KCCQ) and 6-minute walk distance (6MWD) at baseline and weeks 8 and 16.
Results:
Of the 817 patients randomized, 319 (39%) had adequate baseline accelerometer data. Patients with lower levels of physical activity had lower (i.e. worse) KCCQ scores and 6MWD, higher NT-proBNP levels and BMI, worse kidney function, and more diabetes and atrial fibrillation. Baseline accelerometer values had weak correlations with KCCQ summary scores (Pearson r=0.06 to 0.21) and weak to moderate correlations with 6MWD (Pearson r=0.20 to 0.31). The change from baseline to 16 weeks in accelerometer-measured physical activity correlated weakly with the change in KCCQ summary scores (Pearson r=0 to 0.18) and 6MWD (r=0.01 to 0.10).
Conclusions:
In the DETERMINE trials, accelerometer-based measures of physical activity correlated modestly with KCCQ summary scores and 6MWD. Accelerometer-based assessments of physical activity may provide additional information complementing that obtained from standard measures of functional limitation in patients with heart failure
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