西方残障身份发展理论述评及本土化思考

积极的残障身份认同对残障者本身具有重要意义,残障身份发展是残障者实现身份认同的路径,理解残障身份发展的过程也可以让残疾人工作者更好地为残障者提供教育、康复等相关服务。本文通过梳理较为典型的西方残障身份发展理论,结合国内残障身份相关研究及本土社会文化背景展开讨论。文章认为,在西方残障身份发展理论框架下,残障者形成积极身份认同的关键在于认同并融入一套在残障社区内共享的残障文化,而在我国社会文化背景下,目前并没有一套成体系的残障文化,残障者也难以体会残障的文化身份感,国内残障者的身份认同缺少本土残障文化依靠。     

Asian children’s verbal development: A comparison of the United States and Australia

Using longitudinal cohort studies from Australia and the United States, we assess the pervasiveness of the Asian academic advantage by documenting White-Asian differences in verbal development from early to middle childhood. In the United States, Asian children begin school with higher verbal scores than Whites, but their advantage erodes over time. The initial verbal advantage of Asian American children is partly due to their parent’s socioeconomic advantage and would have been larger had it not been for their mother’s English deficiency. In Australia, Asian children have lower verbal scores than Whites at age 4, but their scores grow a faster rate and converge towards those of Whites by age 8. The initial verbal disadvantage of Asian Australian children is partly due to their mother’s English deficiency and would have been larger had it not been for their Asian parent’s educational advantage. Asian Australian children’s verbal scores grow at a faster pace, in part, because of their parent’s educational advantage.     

The effects of individually varying times of observations on growth parameter estimations in piecewise growth model

When using latent growth modeling (LGM), researchers often restrict the factor loadings, while the multilevel modeling (MLM) treats time as a metric variable. However, when individually varying times of observations are concerned in the longitudinal studies, the use of specified loadings would lead to inaccurate estimation. Based on piecewise growth modeling (PGM), this simulation study showed that (i) individually varying times of observations with larger boundaries got worse estimates and model fits when LGM was used; (ii) estimating the PGM across all the simulation situations was robust within MLM, whereas LGM got identically equal estimation with MLM only in the case of time boundaries of ±1 month or shorter; (iii) larger change of slope in piecewise modeling indicated better estimation.     

Assessing the Performance of a Classification‐Based Vulnerability Analysis Model

In this article, a classification model based on the majority rule sorting (MR‐Sort) method is employed to evaluate the vulnerability of safety‐critical systems with respect to malevolent intentional acts. The model is built on the basis of a (limited‐size) set of data representing (a priori known) vulnerability classification examples. The empirical construction of the classification model introduces a source of uncertainty into the vulnerability analysis process: a quantitative assessment of the performance of the classification model (in terms of accuracy and confidence in the assignments) is thus in order. Three different app oaches are here considered to this aim: (i) a model–retrieval‐based approach, (ii) the bootstrap method, and (iii) the leave‐one‐out cross‐validation technique. The analyses are presented with reference to an exemplificative case study involving the vulnerability assessment of nuclear power plants.     

Towards Uniformly Efficient Trend Estimation Under Weak/Strong Correlation and Non‐stationary Volatility

In this paper, we consider the deterministic trend model where the error process is allowed to be weakly or strongly correlated and subject to non‐stationary volatility. Extant estimators of the trend coefficient are analysed. We find that under heteroskedasticity, the Cochrane–Orcutt‐type estimator (with some initial condition) could be less efficient than Ordinary Least Squares (OLS) when the process is highly persistent, whereas it is asymptotically equivalent to OLS when the process is less persistent. An efficient non‐parametrically weighted Cochrane–Orcutt‐type estimator is then proposed. The efficiency is uniform over weak or strong serial correlation and non‐stationary volatility of unknown form. The feasible estimator relies on non‐parametric estimation of the volatility function, and the asymptotic theory is provided. We use the data‐dependent smoothing bandwidth that can automatically adjust for the strength of non‐stationarity in volatilities. The implementation does not require pretesting persistence of the process or specification of non‐stationary volatility. Finite‐sample evaluation via simulations and an empirical application demonstrates the good performance of proposed estimators.     

Optimization of Cascade‐Resilient Electrical Infrastructures and its Validation by Power Flow Modeling

Large‐scale outages on real‐world critical infrastructures, although infrequent, are increasingly disastrous to our society. In this article, we are primarily concerned with power transmission networks and we consider the problem of allocation of generation to distributors by rewiring links under the objectives of maximizing network resilience to cascading failure and minimizing investment costs. The combinatorial multiobjective optimization is carried out by a nondominated sorting binary differential evolution (NSBDE) algorithm. For each generators–distributors connection pattern considered in the NSBDE search, a computationally cheap, topological model of failure cascading in a complex network (named the Motter‐Lai [ML] model) is used to simulate and quantify network resilience to cascading failures initiated by targeted attacks. The results on the 400 kV French power transmission network case study show that the proposed method allows us to identify optimal patterns of generators–distributors connection that improve cascading resilience at an acceptable cost. To verify the realistic character of the results obtained by the NSBDE with the embedded ML topological model, a more realistic but also more computationally expensive model of cascading failures is adopted, based on optimal power flow (namely, the ORNL‐Pserc‐Alaska) model). The consistent results between the two models provide impetus for the use of topological, complex network theory models for analysis and optimization of large infrastructures against cascading failure with the advantages of simplicity, scalability, and low computational cost.     

Navigating the ethical maze in disability research: ethical contestations in an African context

Despite changes in how disability is viewed, ethical requirements for disability research have hardly changed. Some ethical clearance procedures, processes and practices still consider persons with disabilities as not able, creating unease among researchers and research participants with disabilities themselves. This paper considers five ethical contestations arising from research in the area of disability in an African context: positionality, vulnerability, signed consent, anonymity, and research committee composition. We argue that ethical requirements in practice are still largely based on a medical model of disability and propose that culturally sensitive social and human rights models should influence disability research ethics.     

Influence diagnostics for Student-t censored linear regression models

In this paper, we extend the censored linear regression model with normal errors to Student-t errors. A simple EM-type algorithm for iteratively computing maximum-likelihood estimates of the parameters is presented. To examine the performance of the proposed model, case-deletion and local influence techniques are developed to show its robust aspect against outlying and influential observations. This is done by the analysis of the sensitivity of the EM estimates under some usual perturbation schemes in the model or data and by inspecting some proposed diagnostic graphics. The efficacy of the method is verified through the analysis of simulated data sets and modelling a real data set first analysed under normal errors. The proposed algorithm and methods are implemented in the R package CensRegMod.