治理的发轫与嬗变：中国历史视野下的考察

国内学术界一般以西方学术发展史作为背景，把治理作为一种社会互动来进行研究。中国历史视野下的治理，发轫于中国社会生活，其内涵最初主要是为了获取生存资料，即有收成才能够形成秩序也就是治理。从“乂”这个象形文字来看，乂就是治理，其原意为“收割”。不过，围绕生存资料的生产和分配逐渐产生了更加丰富的社会权力关系，而巩固和拓展这种权力关系则使得构筑更加严格的秩序成为必然。神秘感是构筑秩序的重要依据，而赋予仪式以神秘感的则是觋、巫等知识群体，他们将天、帝、王、人通过仪式联系在一起，建构起一套以礼为基础的秩序。但是，西周末年，觋、巫的神秘感被世俗的君权摧毁以后，“君臣秩序”以及由此构筑起来的社会秩序都被颠覆，从而导致“君臣之礼既坏”和“礼坏乐崩”的情形。“以智力相雄长”不仅表现在王室内部、诸侯之间、社会层面上，而且也表现在知识分子身上。从此以后，士绝大多数以追逐爵禄而骄天下为荣，以至于两千多年来一直存在“士贱而君肆”的情形。“君士互动”的治理模式，正是随着神秘主义的礼的崩溃而崩溃的。     

治理效能视角下公民参与公共服务的制度研究

公民参与公共服务的有序性和有效性并非直接相关,可以从有序性和有效性两个维度分析公民参与及其治理效能的关系.为提升治理效能,以程序性规则为核心的制度设计能更为充分地保障公民有序有效参与公共服务,实现公民与政府之间的良性互动与有序合作.基于全过程民主、程序性权利和共同体秩序等公民参与的程序性价值维度考量,再以环境保护领域中的公民参与为例,从公开公示机制、意见表达机制、回应吸纳机制、监督评价机制和责任追究机制等方面构建公民全过程参与制度,并探索公民参与的方法和策略.通过公民参与的程序性价值内化、方法创新与技术转型来推进治理体系和治理能力现代化.     

局部平稳部分线性面板数据模型的统计推断

部分线性模型是一类非常重要的半参数回归模型,由于它既含有参数部分又含有非参数部分,与常规的线性模型相比具有更强的适应性和解释能力。文章研究带有局部平稳协变量的固定效应部分线性面板数据模型的统计推断。首先提出一个两阶段估计方法得到模型中未知参数和非参数函数的估计,并证明估计量的渐近性质,然后运用不变原理构造出非参数函数的一致置信带,最后通过数值模拟研究和实例分析验证了该方法的有效性。     

工业劳动力流动、资本创造与经济地理空间均衡

资本创造模型（CC模型）忽视了要素流动对产业空间分布的影响。而发展的新的资本创造模型则认为资本集聚的过程必然伴随着工业劳动力的流动过程。另外，是资本的实际收益而不是名义收益决定资本是否创造。研究结果表明，随着贸易自由度、工业品支出份额及资本贴现率的变大，替代弹性及资本折旧率的变小，将降低对称结构的稳定性，而提高中心-外围结构的稳定性；经济地理空间的产业均衡是集聚力和分散力相互作用的结果。当企业生产工业品的规模报酬递增程度足够显著，或者工业品支出份额很高时，市场拥挤效应将彻底消失，并转化成为促进产业集聚的动力；突破点与持续点的大小比较可以形成不同的关系，这意味着随着贸易自由度的变化，本文发展的资本创造模型可以体现出多样化的产业空间动态演化行为。     

Estimation of geodesic tortuosity and constrictivity in stationary random closed sets

We investigate the problem of estimating geodesic tortuosity and constrictivity as two structural characteristics of stationary random closed sets. They are of central importance for the analysis of effective transport properties in porous or composite materials. Loosely speaking, geodesic tortuosity measures the windedness of paths, whereas the notion of constrictivity captures the appearance of bottlenecks resulting from narrow passages within a given materials phase. We first provide mathematically precise definitions of these quantities and introduce appropriate estimators. Then, we show strong consistency of these estimators for unboundedly growing sampling windows. In order to apply our estimators to real data sets, the extent of edge effects needs to be controlled. This is illustrated using a model for a multiphase material that is incorporated in solid oxide fuel cells.     

Midwives’ views of caseload midwifery – comparing the caseload and non-caseload midwives’ opinions. A cross-sectional survey of Australian midwives.

BackgroundMidwife-led continuity of care has substantial benefits for women and infants and positive outcomes for midwives, yet access to these models remains limited. Caseload midwifery is associated with professional satisfaction and lower burnout, but also impacts on work-life boundaries. Few studies have explored caseload midwifery from the perspective of midwives working in caseload models compared to those in standard care models, understanding this is critical to sustainability and upscaling.AimTo compare views of caseload midwifery – those working in caseload models and those in standard care models in hospitals with and without caseload.MethodsA national cross-sectional survey of midwives working in Australian public hospitals providing birthing services.FindingsResponses were received from 542/3850 (14%) midwives from 111 hospitals – 20% worked in caseload, 39% worked in hospitals with caseload but did not work in the model, and 41% worked in hospitals without caseload. Regardless of exposure, midwives expressed support for caseload models, and for increased access to all women regardless of risk. Fifty percent of midwives not working in caseload expressed willingness to work in the model in the future. Flexibility, autonomy and building relationships were positive influencing factors, with on-call work the most common reason midwives did not want to work in caseload.ConclusionsThere was widespread support for and willingness to work in caseload. The findings suggest that the workforce could support increasing access to caseload models at existing and new caseload sites. Exposure to the model provides insight into understanding how the model works, which can positively or negatively influence midwives’ views.     

Approximate composite marginal likelihood inference in spatial generalized linear mixed models

Non-Gaussian spatial responses are usually modeled using spatial generalized linear mixed model with spatial random effects. The likelihood function of this model cannot usually be given in a closed form, thus the maximum likelihood approach is very challenging. There are numerical ways to maximize the likelihood function, such as Monte Carlo Expectation Maximization and Quadrature Pairwise Expectation Maximization algorithms. They can be applied but may in such cases be computationally very slow or even prohibitive. Gauss–Hermite quadrature approximation only suitable for low-dimensional latent variables and its accuracy depends on the number of quadrature points. Here, we propose a new approximate pairwise maximum likelihood method to the inference of the spatial generalized linear mixed model. This approximate method is fast and deterministic, using no sampling-based strategies. The performance of the proposed method is illustrated through two simulation examples and practical aspects are investigated through a case study on a rainfall data set.     

Noninformative priors for the between-group variance in the unbalanced one-way random effects model with heterogeneous error variances

For the unbalanced one-way random effects model with heterogeneous error variances, we propose the non-informative priors for the between-group variance and develop the first- and second-order matching priors. It turns out that the second-order matching priors do not exist and the reference prior and Jeffreys prior do not satisfy a first-order matching criterion. We also show that the first-order matching prior meets the frequentist target coverage probabilities much better than the Jeffreys prior and reference prior through simulation study, and the Bayesian credible intervals based on the matching prior and reference prior give shorter intervals than the existing confidence intervals by examples.