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Decriminalization of drug use in the context of HIV: a guidance note. Creating an enabling legal environment for the HIV response for people who use drugs
Full text linkThe decriminalization of drug use and possession for personal use, when implemented effectively, is a critical element in a human rights and public health-based HIV response. The group of countries that have adopted decriminalization models spans all continents. This document brings together different approaches to and experiences of decriminalization of drug use and possession for personal use and provides recommendations for countries to ensure an enabling environment for the HIV response
Technological Reflexivity in Practice: How MAXQDA, NVivo, and ChatGPT Shape Qualitative Survey Analysis
Full text linkThe emergence of Generative Artificial Intelligence (GenAI) into qualitative data analysis software (CAQDAS) intensifies longstanding debates around about how digital tools shape qualitative analysis. This paper foregrounds the concept of technological reflexivity, critically examining how technologies co-produce research practices and claims. Drawing on a reflexive, empirical comparison of MAXQDA, NVivo, and ChatGPT, I examine how different software architectures and GenAI features mediate analytic decisions, researcher-participant relationship and interpretive authority. The analysis uses data from over 1,300 young people (aged 8–25) who responded to a climate-themed qualitative survey imagining the life of a fictional peer in 2050. Findings show how researcher strategies were shaped through distinct software tactics and how GenAI extends existing CAQDAS logics. Its integration raises new challenges for transparency and the relational sensitivity needed to interpret emotionally charged data. Responding to four questions posed by Paulus and Lester (2023) – concerning methods, knowledge production, researcher-participant relationships, and tool design – this paper contributes to calls for methodological transparency and technological reflexivity in qualitative research. It argues for understanding reflexivity as a distributed practice, shared (often unequally) across researchers, tools, and infrastructures. I call for collective responsibility in shaping the ethical and methodological futures of qualitative inquiry
Social Distancing from Innocent Victims by Spatial Distality
No full textAcross several victimization contexts and spatial arrangement methods, nine studies provide triangulating evidence that perceived injustice influences the social distancing of the self from innocent victims by spatial distality. Participants distanced themselves from victims receiving unjust (vs. just) outcomes by placing a symbolic representation of the self further from the victims' names in 2D space (Studies 1a/b). Study 2 replicated this effect using a different spatial arrangement task. Representations of victims’ traits were positioned further from a self-representation when the victims received unjust (vs. just) outcomes, and this distancing effect was stronger for traits participants rated as central to their self-conception. Studies 3a/b provided evidence for the role of perceived injustice in these effects. Participants distanced themselves from innocent (vs. less innocent) victims of unfavourable outcomes (Study 3a). Study 3b confirmed that perceived injustice was greater for unjust (vs. just) outcomes involving innocent victims, and this perception predicted spatial distancing at the level of scenarios only when the victims were innocent. Crucially, Studies 4a/b validated the assumption that spatial distance equates to social distance by finding that participants perceived greater similarity to others whose traits or initials were spatially arranged closer to the self. Finally, Studies 5a/b provide evidence that an event involving two victims was perceived as more unjust for the victim arranged spatially closer to the self. These findings highlight the impact of perceived injustice on distancing from innocent victims (and vice versa) and contribute to the broader understanding of social and spatial representations of self-other (dis)similarity
Inequality Aversion and Prosocial Punishment: Evidence from a One-Shot Public Goods Game
Full text linkThe willingness to engage in costly punishment of free riders (prosocial punishment) is crucial to foster group cooperation and understand public goods provision. While prosocial punishment is common across societies, its motivations remain unclear. Scholars have suggested that people resist inequitable outcomes and willingly bear costs to sanction free riders, seeking a fairer distribution of payoffs. This study tests a key implication of such fairness-driven arguments: if inequality aversion drives prosocial punishment, individuals should punish less when redistribution occurs, as equality concerns would be already satisfied. We conducted a pre-registered 2x2 between-subjects lab experiment (N=320), where participants completed a Social Value Orientation (SVO) task and played a one-shot Public Goods Game (PGG) with a Punishment Stage. We manipulated endowment inequality and the presence of redistributive taxation. Pre-registered analyses show that (1) inequality aversion does not predict prosocial punishment; (2) punishment levels do not significantly differ across treatments. However, exploratory results suggest that under high inequality, redistribution reduces the intensity of punishment towards richer individuals. This could indicate that inequality aversion triggers prosocial punishment only at acute inequality levels
Controlling weak-lensing shear biases from undetected galaxies in the era of Stage IV Surveys
Full text linkGravitational lensing of background galaxies by intervening matter is a powerful probe of the cosmological model. In the era of Stage IV surveys, contamination from galaxies below the detection threshold has emerged as a significant source of bias. Adopting a noise-bias-free machine-learning method to estimate shear, we quantify the impact of faint galaxies for a Euclid-like survey. In our baseline simulations, faint blends induce a multiplicative shear bias of -0.008, well above Euclid's requirement. Similar to previous studies, we find that calibration simulations must include neighbouring galaxies to AB apparent magnitudes as faint as 27.0 (+2.1, -0.9) and within approximately 1.0 (+0.2, -0.2) arcsec of each bright sample galaxy (BSG; the galaxy for which shear is measured). By varying faint galaxy properties, we identify which ones significantly affect shear biases and quantify how well they must be constrained. Crucially, we find that biases not only depend on the mean projected faint-galaxy density and apparent-magnitude distribution across the sample, but also on how these quantities vary with the observed brightness of the BSG. Furthermore, biases are sensitive to radial and tangential alignments and positional anisotropy of faint galaxies relative to BSGs.
By contrast, shear coherence between BSGs and faint galaxies, parallel orientation alignments, and variations in the faint galaxy size–magnitude relation have negligible impact within the parameter ranges explored.
Our results guide calibration simulations and highlight the critical role of deep observations in measuring the properties of faint galaxies
EEG-based methods for diagnosing awareness in disorders of consciousness
Full text linkA brain–computer interface (BCI) is an advanced neurotechnological system that enables direct communication between the brain and the external environment by bypassing conventional neuromuscular pathways. This capability offers valuable insight into the assessment of awareness in acute clinical states. Clinically, diagnosing disorders of consciousness (DOC) remains a significant challenge, largely because current practice relies heavily on behavioral indicators of consciousness —markers that are often ambiguous and prone to misinterpretation. To address these limitations, electrophysiological and neuroimaging techniques have been explored, with electroencephalography (EEG) standing out for its non-invasiveness, portability, high temporal resolution, and robustness. As a result, EEG-based methods and BCI-inspired protocols have emerged as promising tools for improving the diagnosis and prognosis of DOC, particularly in detecting cognitive motor dissociation (CMD), a condition frequently overlooked by standard clinical scales. Despite this promise, the clinical translation of these approaches remains constrained, primarily due to a shortage of sufficiently powered validation studies. In this thesis, I evaluate the effectiveness of several popular EEG-based methods and systematically compare the performance of deep and shallow classification models on a large, novel dataset acquired using a motor imagery (MI)-based command-following paradigm in accessing awareness with DOC patients. Specifically, I extracted measures including classification accuracy, brain rhythms, effective connectivity and the perturbational complexity index PCI, from MI, idling and functional electrical stimulation FES epochs. These were contrasted against Coma Recovery Scale–Revised (CRS-R) scores, the current clinical gold standard. Furthermore, state-of-the-art deep learning models (EEGNet, DeepConvNet, and EEGConformer) were evaluated alongside a shallow classifier, employing leave-one-trial-out cross validation scheme on the full and windowed trial segments. In addition, analysis was evaluated at the sessional level to account for variability in diagnostic states. The findings confirm that EEG contain valuable information regarding the state of awareness of DOC patients. In particular, the classification accuracy and the μ-/β-band separability of MI power spectral density(PSD) features, as well as centro-parietal δ- band connectivity during MI and resting, correlate statistically significantly with CRS-R. Moreover, metric-specific thresholds separating awareness from non-awareness could be determined. I further provide useful insights on the ability of these metrics to detect CMD and rectify the false-negative vulnerability of CRS-R. At the same time, this work highlights the risk of statistical misuse of such metrics, which can lead to over-optimistic assessments of latent awareness. Furthermore, the thesis also reveals that deep learning architectures may be prone to overestimation of results when applied to DOC populations. This research supports the potential of open-loop BCI DOC diagnosis and highlights the need for further development, validation and standardization to establish clinically deployable systems
Working towards a perpetrator-focused approach to domestic abuse: visibility and accountability in theory and practice
Full text linkThis thesis examines domestic abuse agency responses amid recent shifts toward perpetrator-focused approaches. It develops the notions of visibility and accountability as key, measurable concepts through which perpetrator responses can be assessed. Visibility refers to how perpetrators are recognised and acknowledged, while accountability encompasses assigning responsibility and targeting interventions around their needs and risks. Grounded in feminist and power-based theories, the thesis emphasises the need to root perpetrator interventions in a deep understanding of gender, power and control. The research stems from a three-year collaborative project with a London local authority, facilitating an in-depth analysis of practice responses. The study employs mixed methods, integrating quantitative and qualitative analysis of multi-agency processes and practitioner interviews, allowing for a comprehensive evaluation of practice. To my knowledge, this is the first study to combine multiple data sources in this manner with a sole focus on perpetrator efforts and outcomes. The study generates new empirical evidence with key implications for theory, policy and practice, underscoring the need to systematically evaluate responses. It reveals persistent challenges that prevent agencies from enhancing perpetrator visibility and accountability. It depicts a practice landscape shaped by systemic and workforce-related challenges, with deficiencies in effective monitoring and evaluation mechanisms that perpetuate perpetrator invisibility and undermine accountability efforts. Gender dynamics further compound these issues, with female practitioners facing greater difficulties, male perpetrators more likely to evade systems, and female victims more often blamed. Perpetrators’ use of power and control intensifies challenges through the deployment of tactics aimed at reasserting dominance. Despite growing calls to prioritise perpetrators, substantial obstacles persist. The findings outline pathways for advancing perpetrator-facing interventions, practitioner support, local processes, and national systems and data. Only such a coordinated, multi-faceted approach can result in meaningful changes that address the current fragmented and inconsistent state of perpetrator responses
Covariate Augmented CUSUM Bubble Monitoring Procedures
Full text linkWe explore how information from covariates can be incorporated into the CUSUM based real-time monitoring procedure for explosive asset price bubbles developed in Homm and Breitung (2012). Where dynamic covariates are present in the data generating process, the false positive rate of the basic CUSUM procedure, which is based on the assumption that prices follow a univariate data generating process, under the null of no explosivity will not, in general, be properly controlled, even asymptotically. In contrast, accounting for these relevant covariates in the construction of the CUSUM statistics leads to a procedure whose false positive rate can be controlled using the same asymptotic crossing function as employed by Homm and Breitung (2012). Doing so is also shown to have the potential to significantly increase the chance of detecting an emerging bubble episode in finite samples. We additionally allow for time varying volatility in the innovations driving the model through the use of a kernel-based variance estimator
Parent served vegetable portion sizes and perception of food leftovers across different meal combinations: A cross sectional, online study.
Full text linkDespite vegetables being commonly served at UK mealtimes, children’s consumption remains insufficient. Because portion sizes provided by parents predict children’s intake, understanding how parents decide on vegetable portions during meals is critical but underexplored. This study examined whether meal context and food combinations influence parent portion size decisions. In a novel online portion size task, 407 parents (203 female) of 4–8-year-old children selected portions of protein, carbohydrate, and vegetable items across nine meal combinations. Parents then anticipated how much food their child would leave after each meal. Meal factors (food items), child factors (food liking, familiarity, anticipated leftovers, eating traits, gender) and parental factors (mealtime goals and feeding practices) were explored as predictors of parent vegetable portion sizes and anticipated child vegetable leftovers. Smaller vegetable portions were associated with lower perceived child vegetable liking, greater anticipated vegetable leftovers, and parental goals to avoid mealtime stress, whereas goals to serve healthy foods predicted larger portions. Meal combinations had a stronger effect on anticipated vegetable leftovers than on portion sizes. Parents expected more vegetable leftovers when non-vegetable items were highly liked or anticipated to be leftover, while higher vegetable liking and familiarity predicted fewer leftovers. These findings suggest that parents base vegetable portion sizes primarily on expectations about individual foods rather than the overall meal. However, when anticipating leftovers, parents appear to consider the influence of more palatable, non-vegetable items on their child’s vegetable intake. Understanding these decision-making processes may inform strategies to support parents in serving appropriate vegetable portions and encouraging their intake
The annual changes and differences in the physical performance characteristics of sports school students
Full text linkSports schools provide an environment that foster both academic and sporting excellence in youth athletes, yet physical performance during adolescence is strongly influenced by biological growth and maturation. High intensity actions such as sprinting, jumping and changing direction are important in many sports, particularly team sports, but how these characteristics develop across the school year remains unclear, especially in mixed-sex populations. This study explores the seasonal variations in physical performance across an academic year within a sports school, accounting for biological maturation status and sex. A total of 337 different student athletes between the ages 9–19 years (14.10 years ± 1.93) completed anthropometrics assessments, maturity estimations (percentage predicted adult height) and performance testing (20 m sprint, countermovement jump, Pro Agility and 30-15 IFT) at up to ten testing dates across four year (2021–2024). Performance increased with both chronological age and biological maturity in both sexes. In males, sprint, agility, and jump performance increased progressively across age groups, while females demonstrated significantly lower performance at U12 compared to all other age groups yet few differences after that except in 20 m sprint. Females saw maturational differences between pre- and post-PHV in all tests. A significant interaction between test date and maturity was evident in males for CMJ and VO2max yet only a small effect size was evidenced. These findings highlight the importance of tracking sex specific and maturity related changes across the academic year to appropriately support training prescription, particularly in female athletes who may require more targeted developmental opportunities