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1.
This paper explores what happens when corporations engage explicitly in practices of organisational learning not only to become better capitalists by generating ever more innovative ways of maintaining profitability, improving competitiveness and maximising shareholder value, but also to become more responsible corporate citizens in their business practices. In particular, I evaluate the ways in which UK food and clothing retailers are learning to develop their ethical trading programmes in response to political calls for more responsible trading. Thrift’s [Thrift, N., 2005. Knowing Capitalism. Sage, London] notion of ‘knowledgeable, or soft, capitalism’ is adopted to understand the creative and experimental ways in which retailers and their mentors (ethical consultancies, social auditors and multi-stakeholder organisations) are learning to trade ethically. Two specific examples of formal learning spaces experienced by UK food and clothing retailers are examined: (i) training courses on social auditing and (ii) corporate awareness-raising courses on ethical trade. These courses are shown to encompass various participative and affective practices of learning. And while particular limits to the success of these courses are argued to exist, ethical learning practices discussed in this paper are nonetheless suggested to play a role in the making of new, albeit moderate, forms of responsible capitalism. 相似文献
2.
Models of spatial choice behaviour have been around in geography urban planning for decades to assess the feasibility of planning
actions or to predict external (competition) effects on existing destinations. Although these models differ in terms of complexity
and key concepts, they all have in common that spatial choice behaviour is predicted as a function of the attributes of the
choice alternatives and distance or travel time separation only. None of these models do take into account that the attributes
of choice alternatives and travel time may be highly non-stationary and that often the utility that people derive from visiting
a particular location also depends on the choice behaviour of other individuals. Under these circumstances, individuals may
exhibit strategic choice behaviour. That is, they will choose particular choice options taking into account their expected
behaviour of others such as to maximize their own utility. The purpose of the proposed paper is to discuss possible models
of strategic choice behaviour for these urban planning problems. Theory will be outlined and some critical issues in the application
of such models to problems of spatial choice will be discussed. 相似文献
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随着村镇经济建设发展,生活垃圾和工业固体废弃物造成的污染问题日益突出,已经成为制约新农村建设发展和生态文明建设的关键问题,而目前针对乡镇非正规固体废弃物的调查与统计主要依赖全国各乡镇相关部门逐级调查上报,工作量较大。本文基于高分辨率遥感影像,将深度学习模型和条件随机场模型相结合引入到乡镇固体废弃物的提取研究中,探索一种基于深度卷积神经网络的乡镇固体废弃物提取模型。由于固体废弃物在影像上表现为面积小,分布破碎等特点,为了提高工作效率,将模型特分为识别和提取2个部分：① 通过全连接卷积网络（CNN）对固体废弃物进行快速识别判断,筛选感兴趣区域影像块;② 在传统的全卷积神经网络(FCN)的基础上加入条件随机场模型(CRF)提取固体废弃物边界,提高整体分割精度。根据安徽、山西等地区相关部门上报固体废弃物堆放点以及住房与城乡建设部城乡规划管理中心进行野外检查的结果,实验最终识别精度达到86.87%以上;形状提取精度为89.84%,Kappa系数为0.7851,识别与提取精度均优于传统分类方法。同时,该方法已经逐步应用于住房和城乡建设部有关成都、兰州、河北等部分乡镇非正规固体废弃物的核查工作,取得了较为满意的结果。 相似文献
5.
学习向量量化（Learning Vector Quantization,LVQ）神经网络在声学底质分类中具有广泛应用. 常用的LVQ神经网络存在神经元未被充分利用以及算法对初值敏感的问题,影响底质分类精度. 本文提出采用遗传算法（Genetic Algorithms,GA）优化神经网络的初始值,将GA与LVQ神经网络结合起来,迅速得到最佳的神经网络初始权值向量,实现对海底基岩、砾石、砂、细砂以及泥等底质类型的快速、准确识别. 将其应用于青岛胶州湾海区底质分类识别研究中,通过与标准的LVQ神经网络的分类结果进行比较表明,该方法在分类速度以及精度上都有了较大提高. 相似文献
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Natural resource management and conservation programs that promote building capacity and social learning among participants often lead to the formation of learning networks: a type of social network where learning is both a goal and potential outcome of the network. Through forming relationships and sharing information, participants in a learning network build social capital that can help a network achieve social and environmental goals. In this study, we explored social capital in a learning network that emerged through a large-scale marine governance effort, the Coral Triangle Initiative on Coral Reefs, Fisheries, and Food Security. Through a mixture of social network analysis and key informant interviews, we examined the major patterns of information exchange among individuals who had participated in regional learning exchanges; evaluated whether the network's structure resulted in information sharing; and considered implications for strengthening network sustainability, capacity building, and learning. We found that the Regional Exchange network fostered information sharing among participants across national and organizational boundaries. While the network had individuals who were more central to information sharing, the network structure was generally decentralized, indicating potential resilience to changes in leadership and membership. Participants stressed the importance of the knowledge and connections they had acquired through the learning network; however, they expressed doubts regarding its sustainability and stressed the need for a strong coordinating entity. Our findings suggest that conservation learning networks have the ability to bridge cultural divides and promote social learning; however, a strong network coordinator and continuing efforts to support information sharing and learning are crucial to the network's strength and sustainability. The tangible learning and capacity development outcomes cultivated through Regional Exchange network underscore the value of and need to invest in conservation networks that support peer-to-peer learning. 相似文献
8.
针对极端学习机(Extreme Learning Machine,ELM)用于日长(Length-Of-Day,LOD)变化预报过程中,样本输入方式对预报结果的影响进行了研究。采用跨度、连续和迭代3种样本输入方式对日长变化进行预报。结果表明,不同的样本输入方式对预报结果有很大影响,样本按跨度输入的预报精度最低;样本采用连续输入方式在短期和中长期预报中预报精度较高,但计算速度较慢,较适合中长期预报;样本按迭代输入方式的短期预报精度稍优于连续输入方式,而中长期预报精度则不如连续输入方式,但具有较高的预报效率。这对于日长变化的实时快速预报有着较高的现实意义。 相似文献
9.
Adaptive co-management and the paradox of learning 总被引：10，自引：1，他引：9
Much emphasis has been placed on the importance of learning to support collaborative environmental management and achieve sustainability under conditions of social–ecological change. Yet, on-going struggles to learn from experience and respond to complex social–ecological conditions reflect an emerging paradox. Despite widespread support of learning as a normative goal and process, core concepts, assumptions and approaches to learning have been applied in vague and sometimes uncritical ways. Greater specificity with respect to learning goals, approaches and outcomes is required. In response to this gap, we examine five dimensions of the learning paradox in the context of adaptive co-management, where the learning and linking functions of governance are stressed: (i) definitions of learning; (ii) learning goals and expectations; (iii) mechanisms by which learning takes place; (iv) questions regarding who is involved in the process of learning; and (v) the risks and ethical ambiguities faced by different actors expected to willingly participate in a learning process, whether formal or informal. Lessons from experience with a series of cases from the global North and South illustrate the implications of these dimensions. Resolving the dimensions of this learning paradox will require greater attention to capacity-building, recognition of the role of risk, and consideration of how incentives could be used to encourage learning. Further consideration of the role of power and marginality among groups participating in the learning process is also needed, as is more systematic evaluation to monitor and measure learning outcomes. 相似文献
10.
针对基于机器学习的滑坡易发性评价中非滑坡样本选取不规范导致的分类精度较低问题,本文提出联合基于密度的噪声应用空间聚类（Density-Based Spatial Clustering of Applications with Noise,DBSCAN）采样策略和支持向量机（Support Vector Machine,SVM）分类方法的DBSCAN-SVM滑坡易发性评价模型。首先,基于DBSCAN聚类和空间分析选取非滑坡样本;然后,将样本数据代入SVM分类模型进行训练与验证,预测并提取SVM分类中属于滑坡的概率,获得滑坡易发性;最后,以四川省绵阳市为试验区,预测滑坡易发性概率,基于滑坡易发性精度与分级结果等要素,与传统非滑坡样本采集策略的SVM滑坡易发性评价模型进行对比,并结合实际情况对DBSCAN-SVM模型评价结果进行分析。研究结果表明,相比传统SVM滑坡易发性评价模型,本文提出的DBSCAN-SVM滑坡易发性评价模型在高易发区和极高易发区中包含的滑坡样本数量较多,准确率、召回率、AUC、F1分数均得到提高,精度较高。 相似文献