Abstract This perspective paper examines transition pathways that move small‐scale fisheries from vulnerability towards viability. We understand ‘vulnerability to viability transition pathways’ as integrative and one that extends beyond economic concerns to include social, political, cultural and ecological aspects of small‐scale fisheries. Our findings draw on a reflexive and qualitative assessment of country‐specific case studies from across Africa and Asia to collaboratively identify transition pathways reflected in these contexts. Common pathways that emerged included: (1) building governance networks and partnerships; (2) centring small‐scale fisheries tenure and rights; (3) advancing a gender and intersectional perspective on viability pathways; (4) enhancing opportunities for ecologically sensitive and diversified livelihoods; and (5) co‐creating and co‐producing the knowledge required to catalyse transition pathways. Outcomes of this analysis provide context‐specific foundations upon which to further co‐develop a research agenda on small‐scale fisheries vulnerability to viability transitions. Insights from this analysis also contribute to the identification of the transdisciplinary capacities needed to build more viable and resilient small‐scale fisheries in the context of ongoing debates about blue economy expansion, and in relation to country‐level commitments to implement provisions of the FAO small‐scale fisheries guidelines. In advancing a vulnerability to viability pathways lens, this paper frames small‐scale fisheries transitions as governance‐mediated, justice‐oriented, relational and inherently non‐linear processes. Read the free Plain Language Summary for this article on the Journal blog.
In an era of intensifying global competition where nations aggressively pursue economic advancement, the imperative to balance progress with ecological preservation has become paramount. However, the race for advancement should not harm nature or future generations. Our study investigates the drivers that can lead to green growth, aligning with sustainable development principles that integrate economic growth, environmental stewardship, and social equity as per the UN's Sustainable Development Goals (SDGs), across nine Western European countries from 2010 to 2019. By utilizing panel data from reputable sources, this research investigates the influence of globalization, natural resource rents, renewable energy consumption, trade openness, and total energy consumption on green growth. Employing contemporary panel diagnostic tests, cointegration analyses, and fixed- and random-effects models, the study also validates its findings through quantile regression, fully modified ordinary least squares, and dynamic ordinary least squares. Our study has fulfilled its destiny by finding the right drivers. According to various analyses, globalization and trade openness consistently and significantly promote green growth, confirming their potential as reliable mechanisms for achieving green growth. The complex impact of renewable energy consumption and natural resource rents opens a new door for exploration by revealing the transitional barriers, such as initial costs and policy lags, in contrast to maintaining the resource rent tendency. However, the beneficial impact of total energy consumption of carbon and fossil fuel underscores the urgency of effective resource utilization and a shift toward renewable sources to decouple growth from unsustainable consumption before running out.
Medical Entity Recognition (MedER) is an essential NLP task for extracting meaningful entities from the medical corpus. Nowadays, MedER-based research outcomes can remarkably contribute to the development of automated systems in the medical sector, ultimately enhancing patient care and outcomes. While extensive research has been conducted on MedER in English, low-resource languages like Bangla remain underexplored. Our work aims to bridge this gap. For Bangla medical entity recognition, this study first examined a number of transformer models, including BERT, DistilBERT, ELECTRA, and RoBERTa. We also propose a novel Multi-BERT Ensemble approach that outperformed all baseline models with the highest accuracy of 89.58%. Notably, it provides an 11.80% accuracy improvement over the single-layer BERT model, demonstrating its effectiveness for this task. A major challenge in MedER for low-resource languages is the lack of annotated datasets. To address this issue, we developed a high-quality dataset tailored for the Bangla MedER task. The dataset was used to evaluate the effectiveness of our model through multiple performance metrics, demonstrating its robustness and applicability. Our findings highlight the potential of Multi-BERT Ensemble models in improving MedER for Bangla and set the foundation for further advancements in low-resource medical NLP.
This qualitative case study explores how ELT teachers in Bangladesh reconfigured their professional identities during the July Revolution, focusing on the nuanced role of negotiations that unfolded in classrooms amid political upheaval and emotional strain. Drawing on interviews with five university teachers and the analysis of multimodal artefacts, the study employs translanguaging pedagogy and social and emotional learning (SEL) as intersecting frameworks. The findings reveal a spectrum of teacher roles, from cautious witnesses to empathetic mentors, who responded to crisis through translanguaging practices, trauma-informed approaches, and creative multimodal strategies. This relational and ethical stance foregrounds care, presence, and cultural responsiveness as central to teaching in times of disruption. The study calls for embedding emotional literacy and context-sensitive pedagogies in teacher education to support educators in sustaining learning during periods of crisis.
Rare but extreme events in public health, such as sudden dengue outbreaks or surges in urgent cancer referrals, pose disproportionate threats to healthcare systems by overwhelming diagnostic capacity, straining resources, and elevating mortality risks. Traditional statistical approaches, which emphasize average behavior, often fail to capture these high-impact tail risks. This study advances the application of Extreme Value Theory in public health by extending the peaks-over-threshold (POT) framework to short, structured medical time series. We introduce a modified Anderson–Darling L-moments based threshold selection procedure, tailored for small-sample health datasets, which enhances robustness and objectivity compared to conventional graphical diagnostics. Using monthly dengue prevalence data from Bangladesh (2008–2023) and urgent cancer referrals from Wales (2005–2020), we demonstrate a systematic workflow that integrates stationarity testing, STL decomposition, transformation through differencing, and scenario-based tail modeling. Our results show that the modified Anderson–Darling L-moments based method yields stable thresholds and consistent generalized Pareto distribution fits across both datasets, including when analyses are restricted to positive increments, capturing the public health priority of sudden increases rather than declines. Return level estimates quantify the scale of extreme month-to-month surges, offering forward-looking insights into outbreak severity and system demand. Methodologically, this study bridges a gap by adapting POT methods for medical data, while practically, it equips decision-makers with interpretable metrics of extreme health shocks. Limitations include reliance on differencing for non-stationarity and the scope of monthly data, but the framework remains generalizable to other health outcomes. By emphasizing both statistical rigor and actionable interpretation, this research positions Extreme Value Theory as a vital tool for anticipating and managing rare but consequential public health extremes.
