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。     

针对生物识别信息的刑法保护:现实境遇与完善路径——以四川“人脸识别案”为切入点

当前生物识别信息在我国社会中的运用呈现逐年递增的趋势.生物识别信息具备本体特殊性和社会特殊性,这决定了其具备与普通公民个人信息不同的重要性,应受到刑法的特殊保护,但我国既定刑事立法对生物识别信息并未进行任何形式的特殊保护.可运用实质解释的方法,在不违反罪刑法定原则的前提下,充分利用两高《解释》第5条中第1款第10项和第2款第4项这两个兜底条款,将"侵犯生物识别信息5条及以上"认定为"情节严重",将"侵犯生物识别信息50条及以上"认定为"情节特别严重",由此降低针对生物识别信息原本的入罪和法定刑升格的数量,最终实现对生物识别信息的特殊刑法保护.     

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。     

走出离合词现象的迷局——从准定语与准名词现象入手

词的离合现象是语法研究中的老大难问题。文章独辟蹊径,从与词的离析使用密切相关的准定语和准名词入手,根据句子的信息结构原则,解释了词的离合现象的成因。文章指出,话语的信息结构安排是促成句法结构重组的语用推力:话题语的指称化处置诱发了准定语结构的操作,基本不涉及词的离析;评述语受单一新信息原则制约而出现的事件化处置,迫使及物性低的双音词强行拆分以容纳事件化所需要素,是诱发词的离析并催生准定语和准名词的直接动因。文章的观点是,用朴素的眼光看汉语,汉语中并无什么离合词,词的离合只是非形态语中双音词的用法变异,是语用型语言句子信息结构进行整编与重组的副产品。     

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。     

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.     

Accurate computation of single and product moments of order statistics under progressive censoring

An accurate procedure is proposed to calculate approximate moments of progressive order statistics in the context of statistical inference for lifetime models. The study analyses the performance of power series expansion to approximate the moments for location and scale distributions with high precision and smaller deviations with respect to the exact values. A comparative analysis between exact and approximate methods is shown using some tables and figures. The different approximations are applied in two situations. First, we consider the problem of computing the large sample variance–covariance matrix of maximum likelihood estimators. We also use the approximations to obtain progressively censored sampling plans for log-normal distributed data. These problems illustrate that the presented procedure is highly useful to compute the moments with precision for numerous censoring patterns and, in many cases, is the only valid method because the exact calculation may not be applicable.     

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.     

Checking validity of monotone domain mean estimators

Estimates of population characteristics such as domain means are often expected to follow monotonicity assumptions. Recently, a method to adaptively pool neighbouring domains was proposed, which ensures that the resulting domain mean estimates follow monotone constraints. The method leads to asymptotically valid estimation and inference, and can lead to substantial improvements in efficiency, in comparison with unconstrained domain estimators. However, assuming incorrect shape constraints may lead to biased estimators. Here, we develop the Cone Information Criterion for Survey Data as a diagnostic method to measure monotonicity departures on population domain means. We show that the criterion leads to a consistent methodology that makes an asymptotically correct decision choosing between unconstrained and constrained domain mean estimators. The Canadian Journal of Statistics 47: 315–331; 2019 © 2019 Statistical Society of Canada