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Black teachers’ experiences of reporting racist incidents in English schools: Lessons for school leaders
Get PDFIn 2020, the Runnymede report Race and Racism in English Secondary Schools found that clear anti-racist policies were needed to address the institutional and systematic disadvantaging of racially minoritised staff and students. This article explores Black teachers’ experiences of reporting racist incidents in English schools as a basis for considering the ethical, legal and policy responsibilities of those in positions of senior leadership. Using a counter-story approach, we present a composite narrative based on Black teachers’ experiences and focus on school leaders’ responses to reports of racist incidents. This provides a critical vantage point for considering how those in positions of leadership can deliver on commitments to the safety and well-being of racially minoritised staff and students. Implications for policy and practice are presented at the school and national level
A research landscape of agentic AI and large language models: Applications, challenges and future directions
Get PDFAgentic AI and Large Language Models (LLMs) are transforming how language is understood and generated while reshaping decision-making, automation, and research practices. LLMs provide underlying reasoning capabilities, and Agentic AI systems use them to perform tasks through interactions with external tools, services, and Application Programming Interfaces (APIs). Based on a structured scoping review and thematic analysis, this study identifies that core challenges of LLMs, relating to security, privacy and trust, misinformation, misuse and bias, energy consumption, transparency and explainability, and value alignment, can propagate into Agentic AI. Beyond these inherited concerns, Agentic AI introduces new challenges, including context management, security, privacy and trust, goal misalignment, opaque decision-making, limited human oversight, multi-agent coordination, ethical and legal accountability, and long-term safety. We analyse the applications of Agentic AI powered by LLMs across six domains: education, healthcare, cybersecurity, autonomous vehicles, e-commerce, and customer service, to reveal their real-world impact. Furthermore, we demonstrate some LLM limitations using DeepSeek-R1 and GPT-4o. To the best of our knowledge, this is the first comprehensive study to integrate the challenges and applications of LLMs and Agentic AI within a single forward-looking research landscape that promotes interdisciplinary research and responsible advancement of this emerging field
Paying forward - The new lights press model supporting artists’ books and print-based communities
Get PDFNewLights Press was founded by artist Aaron Cohick in 2000 in Baltimore, USA as a means of publishing his own practice - artists' books and experimental printmaking, as well as works by other artists and writers, all printed and bound by hand in house. Currently based in Louisville, Kentucky Cohick established The NLP Public Goods Grants in 2023 as "an experiment in community-based income redistribution, resource sharing, and care. That “public good” could be resources, tools, online platforms, programming, classes, etc. - the basic criteria are to be broad, open, and inclusive." I spoke to Cohick about the aims of this opportunity for artists and how it has developed over the last two years, and to its most recent awardee
High-throughput biodiversity surveying sheds new light on the brightest of insect taxa
Get PDFDNA metabarcoding of species-rich taxa is becoming a popular high-throughput method for biodiversity inventories. Unfortunately, its accuracy and efficiency remain unclear, as results mostly pertain to poorly known taxa in underexplored regions. This study evaluates what an extensive sampling effort combined with metabarcoding can tell us about the lepidopteran fauna of Sweden—one of the best-understood insect taxa in one of the most-surveyed countries of the world. We deployed 197 Malaise traps across Sweden for a year, generating 4749 bulk samples for metabarcoding, and compared the results to existing data sources. We detected more than half (1535) of the 2990 known Swedish lepidopteran species and 323 species not reported during the sampling period by other data providers. Full-length barcoding confirmed three new species for the country, substantial range extensions for two species and eight genetically distinct barcode variants potentially representing new species, one of which has since been described. Most new records represented small, inconspicuous species from poorly surveyed regions, highlighting components of the fauna overlooked by traditional surveying. These findings demonstrate that DNA metabarcoding is a highly efficient and accurate biodiversity sampling method, capable of yielding significant new discoveries even for the most well known of insect faunas
Sowing the seeds
Get PDFSowing the seeds: artists' books that can help bring us closer to natureAs we notice Spring edging towards summer in the northern hemisphere, and start emerging to explore the pathways forests, hedgerows and fields we can consider the ways book artists use their skills to bring us into these spaces ahead of the season
A systematic review and meta-analysis of the effectiveness of social norms messaging approaches for improving health behaviours in developed countries
No full textSocial norms approaches have been widely applied in health promotion, as a cost-effective behaviour-change strategy, but have been little evaluated as a whole. We conducted a pre-registered systematic review and meta-analysis of randomised controlled trials using social norms messaging in developed countries targeted at changing health behaviours among 16+ year-olds, to evaluate their effectiveness. Relevant studies were identified through searches in PsycINFO, Medline, Embase, Web of Science, TRIP, Cochrane, and grey literature sources. Risk of bias was assessed independently by two reviewers using the Cochrane RoB 2 tool. A random-effects meta-analysis standardized effect sizes to Cohen’s d, assessed heterogeneity with I², and applied Robust Bayesian Meta-Analysis to adjust for publication bias. Searches resulted in 89 studies (n = 85,759), which exhibited a small effect of social norms messaging on health behaviours (Cohen’s d = 0.16, 95% CI [0.11, 0.22], p < .001). However, once controlling for publication bias, this effect disappeared. We conducted moderator analyses that showed no significant differences from the overall effect for different types of social norm messages, delivery modalities, health domains, or target populations. The review is limited by the lack of studies assessing whether normative information changed participant perceptions, inconsistent use of manipulation checks, and high heterogeneity across studies in terms of target behavior, population, and intervention delivery, affecting the robustness of conclusions. Our analysis suggests that when appropriately controlling for publication bias social norms messages are not effective at improving health behaviours. Thus, future attempts at improving public health should focus on alternative approaches
The needs, challenges, and priorities for advancing global flood research
Get PDFIn recent years, numerous flood events have caused loss of life, widespread disruption, and damage across the globe. These devastating impacts highlight the importance of a better understanding of flood generating processes, their impacts, and their variability under climate and landscape changes. Here, we argue that the ability to better model flooding is underpinned by the grand challenge of understanding flood generation mechanisms and potential impacts. To address this challenge, the World Meteorological Organization‐Global Energy and Water Exchanges (GEWEX) Hydrometeorology Panel (GHP) aims to establish a Global Flood Crosscutting project to propagate flood modeling and research knowledge across regions and to synthesize results at the global scale. This paper outlines a framework for understanding the dynamics and impacts of runoff generation processes and a rationale for the role of a Global Flood Crosscutting project to address these challenges. Within this Global Flood Crosscutting project, we will establish a common terminology and methods to enable the global research community to exchange knowledge and experiences, and to design experiments toward developing actionable recommendations for more effective flood management practices and policies for improved resilience. This harmonization of rich perspectives across disciplines will foster the co‐production of knowledge primed to advance flood research, particularly in the current period of heightened climate variability and rapid change. It will create a new transdisciplinary paradigm for flood science, wherein different dimensions of mechanistic understanding and processes are rigorously considered alongside socioeconomic impacts, early warning communications, and longer‐term adaptation to alleviate flood risks in society
Denoise Extreme Learning Machine (DELM) for occupancy estimation in higher education spaces based on CO2 levels
No full textThis research employs a two-stage approach. Initially, a literature review identified that HVAC management in lecture halls is often energy inefficient and fails to maintain optimal conditions for health, comfort, and learning due to discrepancies between estimated and actual occupancy. Accurately estimating real-time occupancy in higher education buildings is crucial but challenging due to complex and dynamic patterns. In the second stage, the research developed, tested, and validated a new DELM (Denoise Extreme Learning Machine) algorithm for accurate real-time occupancy estimation using only occupancy data and CO2 measurements. This algorithm aims to enhance student health, comfort, and performance while reducing energy consumption, and the associated economic cost and CO2 emissions. Therefore, this research is timely and relevant amidst current efforts to address climate change and improve well-being.The performance of standard Extreme Learning Machine (ELM) are typically affected by noisy datasets, the proposed approach addresses this issue by combining Extreme Learning Machine (ELM) and Denoising Stacked Autoencoder (DSAE) to effectively remove noise from CO2 data and achieve high estimation accuracy while maintaining computational efficiency. In addition, this study introduced nine engineered features design to capture a range of occupancy scenarios, thereby enhancing the generalisability of the algorithm. The DELM algorithm’s ability to accurately estimate occupancy was validated across four different scenarios of use and number of occupants, demonstrating its accuracy in those scenarios, achieving 91% accuracy (15% improvement compared to ELM), in 300-person conference rooms; 93% accuracy (18% improvement) in 100-person conference rooms; and 98% accuracy in a 25-person office. The DELM algorithm outperforms ELM in stability and accuracy. This study also confirms that using a single environmental parameter can result in accurate occupancy estimation
Taxation, Human Rights, and Sustainable Development: Global South Perspectives
No full textThis book investigates the relationship between human rights and taxation, exploring how human rights have been impeded or enhanced through tax laws and policies, and what this means for sustainable development in the Global South.Drawing on cases from across the Global South, the book demonstrates the benefits of embedding human rights into tax policies and legislation. The authors not only highlight the role of legislative measures and other human rights regulations in the realisation of international treaty rights but also argue that it creates an environment whereby individuals feel duty-bound to pay taxes, when necessary, thereby securing a sustainable revenue source for the state to meet their socio-economic responsibilities. The book investigates key topics such as compliance, redistribution, e-commerce, tax havens, and the role of key stakeholders.This book will be useful for researchers from across the fields of law, human rights, taxation, and sustainable development
Willing to be the change: Perceived drivers and barriers to participation in urban smart farming projects
Get PDFPsychological research on perceptions on urban smart farming is scarce, especially in a Global South context. To reach wide acceptance of urban smart farming and create effective strategies for the implementation of this innovative technology, we need insights into people’s perceptions. In this article, we investigate the factors that motivate or hinder people to engage in community-led urban smart farming projects. We present a systematic assessment of perceived drivers and barriers for urban smart farming, based on a survey study in three African countries, Nigeria, South Africa and Zambia. Using multiple regression analysis, we could identify country-specific drivers and barriers. People’s demographics have been found to play less of a role in predicting intentions to be involved in urban smart farming projects. We recommend considering the human dimension when promoting innovative technologies such as urban smart farming and encourage practitioners to assess each region individually when promoting innovative farming techniques