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Mountain regions are complex social–ecological–technological systems whose dynamics and challenges can influence lives and livelihoods way beyond the local scale. To capture, represent, and tackle these challenges, the Global Mountain Safeguard Research (GLOMOS) program has developed a systemic approach to understanding and reducing risks in mountain communities. Drawing on 6 years of transdisciplinary research, this paper presents the approach and highlights its main outcomes and lessons learned. GLOMOS advances holistic and context-sensitive studies for resilient mountain communities through 3 main research lines, directed at integrating climate and disaster risk assessment, recognizing diverse knowledge systems for preparedness, and facilitating sustainable water catchment management. The findings emphasize the importance of interconnected, equitable adaptation measures that address the systemic nature of mountain risks and support sustainable development across these vital landscapes.

Research shows that despite organisational efforts to achieve privacy compliance, privacy breaches continue to rise. The extant studies on organisational privacy compliance concentrate on the extent to which privacy threats can be alleviated through a combination of technical and human controls and the positive (and often intended) influences of these controls. This focus inadvertently neglects unintended consequences such as impeded workflow in medical practices. To address this research conflict, this study uses an interpretive grounded theory research approach to investigate the consequences of privacy safeguard enactment in medical practices, including whether it influences their ability to meet privacy requirements and whether workflows are impeded. Our central contribution is a theoretical framework, the unintended consequences of privacy safeguard enactment (UCPSE) framework, which explicates the process by which privacy safeguards are evaluated and subsequently bypassed and the resulting influence on organisational compliance. The UCPSE highlights the importance of the imbalance challenge, which is the result of unintended consequences outweighing the intended consequences of privacy safeguard enactment. Failure to address the imbalance challenge leads to the adoption of workarounds that may ultimately harm the organisation's privacy compliance. Despite several research calls, the consequences and effectiveness of organisational privacy efforts are largely missing from both information systems and health informatics research. This study is one of the first attempts to both systematically identify the impacts of privacy safeguard enactment and to examine its implications for privacy compliance in the healthcare domain. The findings also have practical implications for healthcare executives on the UCPSE and how they could alleviate the imbalance challenge to thwart workarounds and the subsequent negative effects on privacy compliance.

According to transaction cost economics (TCE) reasoning, firms choose equity (as opposed to non-equity) foreign market entry modes to safeguard specific assets. The present paper contextualizes the well-researched relationship between asset specificity and foreign market entry mode choice by introducing knowledge safeguards (international experience, host-country networks, and imitation) and institutional safeguards (property rights protection and cultural proximity) as alternative mechanisms for securing a firm's specific assets. Testing our hypotheses on a sample of 206 small and medium-sized enterprises, we find that knowledge safeguards and institutional safeguards weaken the effect of asset specificity on the choice of equity foreign market entry modes. Contextualizing the relationship between asset specificity and foreign market entry mode choice helps to enhance our understanding of the scope conditions of TCE-based entry mode studies and beyond.

Removal of 99 TcO 4 − from legacy defense nuclear tank waste at Savannah River Site is highly desirable for the purpose of nuclear safety and environmental protection, but currently not achievable given the extreme conditions including high alkalinity, high ionic strength, and strong radiation field. Herein, we present a potential solution to this long-term issue by developing a two-dimensional cationic metal organic framework SCU-103, showing ultrahigh stability in alkaline aqueous media and great resistance to both β and γ radiation. More importantly, it is very effective for 99 TcO 4 − separation from aqueous media as demonstrated by fast exchange kinetics, high sorption capacity, and superior selectivity, leading to the successful removal of 99 TcO 4 − from actual Savannah River Site high level tank waste for the first time, to the best of our knowledge. In addition, the uptake mechanism is comprehensively elucidated by molecular dynamics simulation and density functional theory calculation, showing a unique chemical recognition of anions with low charge density. Separation of 99 TcO 4 − from nuclear waste at the Savannah River Site is hampered by the extreme conditions. Here, the authors propose a solution by developing an alkaline-resistant metal organic framework material featuring unique recognition sites for selective incorporation of 99 TcO 4 − anions.

Balancing agendas for climate mitigation and environmental justice continues to be one of the key challenges in climate change governance mechanisms, such as Reducing Emissions from Deforestation and Degradation (REDD+). In this paper we apply the three-dimensional environmental justice framework as a lens to examine the REDD+ process in the Lao People’s Democratic Republic (Laos) and the REDD+ social safeguards. We focus particularly on challenges to justice faced by marginalized communities living in forest frontier areas under an authoritarian regime. Drawing on policy analysis and open-ended interviews across different policy levels, we explore procedural, distributional, and recognitional justice across the REDD+ policy levels in Laos. We find that REDD+ social safeguards have been applied by both donors and state actors in ways that facilitate external control. We underscore how authoritarian regime control over civil society and ethnic minority groups thwarts justice. We also highlight how this political culture and lack of inclusiveness are used by donors and project managers to implement their projects with little political debate. Further obstacles to justice relate to limitations inherent in the REDD+ instrument, including tight schedules for dealing with highly sensitive socio-political issues under social safeguards. These findings echo other research but go further in questioning the adequacy of safeguards to promote justice under a nationally driven REDD+. We highlight the importance of recognition and political context, including aspects such as power relations, self-determination and self-governance of traditional or customary structures, in shaping justice outcomes.

An essential first step in planning a confirmatory or a replication study is to determine the sample size necessary to draw statistically reliable inferences using power analysis. A key problem, however, is that what is available is the sample-size estimate of the effect size, and its use can lead to severely underpowered studies when the effect size is overestimated. As a potential remedy, we introduce safeguard power analysis, which uses the uncertainty in the estimate of the effect size to achieve a better likelihood of correctly identifying the population effect size. Using a lower-bound estimate of the effect size, in turn, allows researchers to calculate a sample size for a replication study that helps protect it from being underpowered. We show that in most common instances, compared with nominal power, safeguard power is higher whereas standard power is lower. We additionally recommend the use of safeguard power analysis to evaluate the strength of the evidence provided by the original study.

Abstract Objectives Machine learning (ML) and natural language processing have great potential to improve efficiency and accuracy in diagnosis, treatment recommendations, predictive interventions, and scarce resource allocation within psychiatry. Researchers often conceptualize such an approach as operating in isolation without much need for human involvement, yet it remains crucial to harness human-in-the-loop practices when developing and implementing such techniques as their absence may be catastrophic. We advocate for building ML-based technologies that collaborate with experts within psychiatry in all stages of implementation and use to increase model performance while simultaneously increasing the practicality, robustness, and reliability of the process. Methods We showcase pitfalls of the traditional ML framework and explain how it can be improved with human-in-the-loop techniques. Specifically, we applied active learning strategies to the automatic scoring of a story recall task and compared the results to a traditional approach. Results Human-in-the-loop methodologies supplied a greater understanding of where the model was least confident or had knowledge gaps during training. As compared to the traditional framework, less than half of the training data were needed to reach a given accuracy. Conclusions Human-in-the-loop ML is an approach to data collection and model creation that harnesses active learning to select the most critical data needed to increase a model’s accuracy and generalizability more efficiently than classic random sampling would otherwise allow. Such techniques may additionally operate as safeguards from spurious predictions and can aid in decreasing disparities that artificial intelligence systems otherwise propagate.

Purpose Drawing upon insights from knowledge-based theory and the learning perspective, this study aims to explore safeguarding strategies in open innovation. Geographic diversity and collaborative breadth can effectively protect proprietary innovations that limit knowledge leakage concerns. Design/methodology/approach Using a cross-industry sample from the Taiwanese Technological Innovation Survey III, which covered 1,519 firms, the authors investigate the conditions under which partnership portfolios affect radical innovation. Findings The findings suggest that the partnership portfolio has an inverted U-shaped influence on radical innovation and that this relationship is moderated by geographic diversity and collaborative breadth. This work identifies a balance in the tension between diverse partnership portfolios and knowledge leakage with regard to open innovation activities. Practical implications This study provides senior managers with an indication of the relationships between partnership portfolios and innovative knowledge protection, identifying the geographic diversity and collaborative breadth that serve as safeguards to prevent leakages of a firm’s innovative knowledge. Originality/value This study makes an original contribution to the empirical exploration of innovation knowledge protection and provides new insights into the field of open innovation. The authors, thus, balance the tension between partnership portfolios and knowledge leakage.

The ‘causal link’ requirement for the adoption of safeguard measures, under Article 4.2(b) of the Agreement on Safeguards, has been one of the most controversial disciplines under the WTO Agreement. Some critics, such as Alan O. Sykes, point out logical and practical defects in the WTO panels’ and the Appellate Body’s findings on this topic. This article attempts to clarify the requirements of the ‘causal link’, adopting a new microeconomic analytical model that accounts for the precise conditions of competition between imported and domestic products. The analysis reveals that the ‘causal link’ requirement calls for a demonstration that the factors falling into the category of ‘unforeseen developments’ under Article XIX:1(a) of the GATT 1994 have caused both increased imports and domestic injury.

This viewpoint article first explores the ethical challenges associated with the future application of large language models (LLMs) in the context of medical education. These challenges include not only ethical concerns related to the development of LLMs, such as artificial intelligence (AI) hallucinations, information bias, privacy and data risks, and deficiencies in terms of transparency and interpretability but also issues concerning the application of LLMs, including deficiencies in emotional intelligence, educational inequities, problems with academic integrity, and questions of responsibility and copyright ownership. This paper then analyzes existing AI-related legal and ethical frameworks and highlights their limitations with regard to the application of LLMs in the context of medical education. To ensure that LLMs are integrated in a responsible and safe manner, the authors recommend the development of a unified ethical framework that is specifically tailored for LLMs in this field. This framework should be based on 8 fundamental principles: quality control and supervision mechanisms; privacy and data protection; transparency and interpretability; fairness and equal treatment; academic integrity and moral norms; accountability and traceability; protection and respect for intellectual property; and the promotion of educational research and innovation. The authors further discuss specific measures that can be taken to implement these principles, thereby laying a solid foundation for the development of a comprehensive and actionable ethical framework. Such a unified ethical framework based on these 8 fundamental principles can provide clear guidance and support for the application of LLMs in the context of medical education. This approach can help establish a balance between technological advancement and ethical safeguards, thereby ensuring that medical education can progress without compromising the principles of fairness, justice, or patient safety and establishing a more equitable, safer, and more efficient environment for medical education.