Organizational learning during policy implementation: lessons from U.S. forest planning

ABSTRACT

Learning is critical for land management agencies implementing new policies in the face of rapid social and ecological change. We investigated learning in the U.S. Forest Service as it implemented new planning regulations. Our research objectives were to: (1) identify collective learning processes and outcomes during this time, and (2) understand factors within the organization supporting or impeding learning. Based on participant observation and 25 interviews with planning personnel, we found evidence of collective learning on individual national forests and across the organization. Several factors helped the agency act as a ‘learning organization,’ including internal networks and tools for information sharing, and meetings for staff to exchange lessons learned. Learning was compromised by limited time and capacity, and lack of internal clarity about balancing the desire for innovation with the need to ensure legal compliance and meet deadlines. This work contributes to the empirical foundations of collective learning theory, allowing us to identify learning processes and outcomes at multiple levels in a public organization, and identifying topics for future research. Based on our exploration of organizational learning, we offer suggestions for how to effectively support learning during times of new policy implementation.     

Public Nutrition in Emergencies: An Overview of Debates, Dilemmas and Decision-making

This paper introduces and discusses the main themes and issues arising from the workshop 'International Public Nutrition in Emergencies: The Potential for Improving Practice'.
Good co-ordination within the nutrition sector of the international humanitarian response system has led to a range of achievements in recent years. Major constraints to improving programme impact remain, however, including misconceptions about the scope of nutrition among the wider humanitarian system, which tends to give it a narrow focus on malnutrition and feeding people. In contrast to this limited view, the Public Nutrition approach brings a more broad-based emphasis to assessing and responding to nutritional problems in emergencies, and takes into account the wider social, economic and political causes of malnutrition.
Six case study presentations illustrated the various components of a Public Nutrition approach, including in-depth assessment, analysis and tailoring programmes accordingly. Additional presentations considered the nature of vulnerability, the concept of Public Nutrition, the responsibilities for addressing nutritional problems and some of the operational tools and frameworks in current use.
Participants agreed on the necessity of raising levels of awareness and understanding among all actors in the humanitarian sphere about the impact of their actions on nutrition. Strategies for achieving this included developing better multi-sectoral working relationships and also strengthening relationships with donors and key decision-makers in the humanitarian system. Other related strategies included institutional learning, training and capacity building, particularly in relation to institutions based in developing countries and building upon initiatives such as the Sphere Project, which has successfully brought together the various actors within the humanitarian system in order to improve quality of response.     

基于CDIO的高职市场营销专业教学模式创新与实践研究

在市场营销专业教学中,通过借鉴CDIO教学理念,建立三系统、四模块教学体系,制定基于项目的"做中学"的教学内容,采用行为导向的六步教学法引导学生主动学习,通过多元化方式进行评价,加强机制建设与教学团队建设。     

The role of science-policy interface in sustainable urban water transitions: Lessons from Rotterdam

This paper investigates the role of the science-policy interface in leveraging transitions to sustainable urban water management. The paper presents a case study of the Dutch city of Rotterdam, which is increasingly regarded as a global leader in adaptive and resilient urban water management. The analysis reveals that Rotterdam’s transition has occurred incrementally over the past 15 years, driven by policy entrepreneurs: largely municipal policy makers and policy practitioners. Strategic use of the science-policy interface (SPI) has facilitated the development of innovative solutions to achieve policy goals and created the enabling conditions necessary for transformative change. The Rotterdam case suggests that an effective SPI requires: (1) compelling water narrative; (2) cross-sectoral collaboration; (3) co-production of knowledge; (4) experiential evidence-based learning; (5) strategic use of trusted scientists; (6) fostering networks; and (7) generating business from science-based innovation. Rotterdam’s strategic approach to knowledge and innovation coupled with a new narrative around water sets it apart from many other cities and adds a new dimension to debates regarding enabling factors for advancing sustainable practices. These findings will be of interest to those engaged in urban water management policy and practice, environmental governance, and debates over transitions more broadly.     

基于SA-ELM的爆破震动速度预测

针对爆破震动速度与其影响因素之间的复杂非线性,结合模拟退火算法(SA)的全局寻优性,提出了一种新的SA-ELM算法.以矿山周边建筑物爆破震动实测数据作为训练样本,选取总药量、最大段药量、测点与爆破点距离、地面震动特性、建筑物震动特性等8个影响因素作为输入变量,建立了爆破震动速度预测的SA-ELM模型.模型训练值和预测值与实测值的均方误差(MSE)分别为0.20和3.26,平均相对误差控制在5％以内,显示出该模型具有良好的训练精度和泛化能力.对比传统ELM模型,SA-ELM模型不但提高了精度和泛化能力,而且降低了隐层节点数变化对训练结果的影响,提高了模型的稳定性.     

Adaptive co-management and network learning in the Room for the River programme

Adaptive co-management and learning are paramount for integrated flood risk management. Relevant literature focuses on adaptation at the level of physical and societal systems. The level of projects and programmes is largely overlooked, but they comprise interventions that adapt our physical systems and they provide opportunities for learning to contribute to transitions of societal systems. This paper aims to increase understanding on how learning takes place and can be stimulated within a programme. The mixed-method case study of Room for the River, a €2.3 billion programme for flood risk management, shows that a programme can be organised using various governance arrangements to stimulate learning and be a means for adaptive co-management to deliver upon environmental objectives.     

Development and Operational Testing of a Super‐Ensemble Artificial Intelligence Flood‐Forecast Model for a Pacific Northwest River

Coastal catchments in British Columbia, Canada, experience a complex mixture of rainfall‐ and snowmelt‐driven contributions to flood events. Few operational flood‐forecast models are available in the region. Here, we integrated a number of proven technologies in a novel way to produce a super‐ensemble forecast system for the Englishman River, a flood‐prone stream on Vancouver Island. This three‐day‐ahead modeling system utilizes up to 42 numerical weather prediction model outputs from the North American Ensemble Forecast System, combined with six artificial neural network‐based streamflow models representing various slightly different system conceptualizations, all of which were trained exclusively on historical high‐flow data. As such, the system combines relatively low model development times and costs with the generation of fully probabilistic forecasts reflecting uncertainty in the simulation of both atmospheric and terrestrial hydrologic dynamics. Results from operational testing by British Columbia's flood forecasting agency during the 2013‐2014 storm season suggest that the prediction system is operationally useful and robust.     

Influencing diversity beliefs through a personal testimonial,promotion-focused approach

Although researchers have highlighted the importance of diversity beliefs (i.e., team members' perceived value of diversity) for the elaboration of information in teams, little attention has been paid to whether and how diversity beliefs can be shaped. Drawing on theory and research on team diversity beliefs, we propose that diversity beliefs are more effectively influenced by interventions using a promotion (compared with a prevention) focus toward diversity and personal testimonial (compared with factual) knowledge. Results from an experiment conducted with 175 teams revealed that both a promotion focus and personal testimonial knowledge independently contributed to more positive diversity beliefs and consequently increased team elaboration of task-relevant information as well as integration of different perspectives. Our results reveal key factors that can influence diversity beliefs and underscore the pivotal role of diversity beliefs in improving the extent to which team members elaborate information and integrate diverse perspectives.     

Machine learning based quantitative consequence prediction models for toxic dispersion casualty

Incidental release of toxic chemicals can pose extreme danger to life in the vicinity. Therefore, it is crucial for emergency responders, plant operators, and safety professionals to have a fast and accurate prediction to evaluate possible toxic dispersion life-threatening consequences. In this work, a toxic chemical dispersion casualty database that contains 450 leak scenarios of 18 toxic chemicals is constructed to develop a machine learning based quantitative property-consequence relationship (QPCR) model to estimate the affected area caused by toxic chemical release within a certain death rate. The results show that the developed QPCR model can predict the toxic dispersion casualty range with root mean square error of maximum distance, minimum distance, and maximum width less than 0.2, 0.4, and 0.3, which indicates that the constructed model has satisfying accuracy in predicting toxic dispersion ranges under different lethal consequences. The model can be further expanded to accommodate more toxic chemicals and leaking scenarios.     

基于视频分析的空管员违规行为识别方法*

为利用视频数据对空管员违规行为进行智能化分析,降低不安全事件发生率,提出2阶段的违规行为识别模型（AR-ResNeXt）,基于实地调研构建空管员视频数据集,利用最小化动态多实例学习损失函数和中心损失函数,获得违规行为检测的判别特征表示,结合异常回归网络和ResNeXt网络,完成对空管员违规行为的时序区间检测与动作分类。研究结果表明:AR-ResNeXt模型在自制数据集中,其帧级AUC达到82.9%,分类准确率达到87.8%,可准确识别空管员发生违规行为的时序区间并进行分类,研究结果可为保障空中交通安全奠定基础。