提高体育专业学生英语学习能力的调查分析

对省内外七所高校不同体育专业学生英语学习能力的全面调查,发现体育专业学生英语能力与其心智发展特点有关;根据这种特点,采取相应的学法教法对策,使他们的英语学习能力得到迅速提高,并为特殊专业学生的英语教学提供了理论和实践依据。     

Making the Best of a Bad Job? Female Part‐Timers’ Orientations and Attitudes to Work

This article uses data from 50 qualitative interviews with female part‐time workers in low‐level jobs in Britain to illustrate their attitudes, orientations and aspirations towards paid work. The research reports two main findings. Firstly, even female part‐time workers at the bottom end of the occupational structure are not a homogeneous group of workers. Secondly, they are not all highly satisfied with their jobs. The article argues that many women (especially those in working‐class households) still do not have a genuine choice between ‘family work’ and ‘market work’. It concludes by presenting a theoretical model of orientations to work.     

On the preservation of copula structure under truncation

The author characterizes the copula associated with the bivariate survival model of Clayton (1978) as the only absolutely continuous copula that is preserved under bivariate truncation.     

Correlates of honor ratings in a clinical clerkship employing a faculty forum evaluation system

5he purpose of this study was to determine whether or not suspected variables affected a surgery clerk's chances of being awarded an honor rating. Findings indicated a significant relationship between a student receiving an honor rating and his or her preceptor's predetermined level of student advocacy, the number of completed patient interview and physical examination write-ups, and final examination scores. There was no significant relationship found between honor ratings and the preceptor's status, the sequence in which the student was discussed or the length of time spent discussing the student at the final evaluation meeting, or the number of clinical faculty present at the meeting. These results lend support to the faculty forum evaluation approach, but suggest a need for further scrutiny of some influencing variables to ensure all students are fairly considered and honor ratings judiciously awarded.     

Evaluating a private foundation's health program

Public policy decisions in health are increasingly difficult and expensive. Although there will never be enough information available, private foundations can help to bridge the most important gaps in knowledge. Larger foundations may also wish to respond to those who doubt the value of foundation activities. This article reviews the experiences over the past eight years of The Robert Wood Johnson Foundation in employing evaluation and related social research procedures in the planning and implementation of a major philanthropic effort to improve the health and medical care of Americans. Discussed are the still evolving Foundation evaluation framework, the unanticipated problems in undertaking specific evaluations, and the substantive findings of some of the studies.     

Network analysis in evaluation: some words of caution

Network analysis, a methodology derived from general systems' theory, can be utilized as a community mental health administrative-evaluation procedure. Evaluation parameters derived from analysis of patient data as they “flow” through a network of agency services, provide measures of systemic functioning. These parameters include “longest paths” and various ratio relationships as evaluation measures of service delivery. The limitations of network analysis are examined by means of conceptual analyses, and phenomena that emerged from research experience. The necessity for both quantitative and qualitative data to ensure a meaningful evaluation of mental health services is explained. Conclusions about the value of the network analysis approach are considered.     

The Assessment of Probability Distributions from Expert Opinions with an Application to Seismic Fragility Curves

A method is developed for estimating a probability distribution using estimates of its percentiles provided by experts. The analyst's judgment concerning the credibility of these expert opinions is quantified in the likelihood function of Bayes'Theorem. The model considers explicitly the random variability of each expert estimate, the dependencies among the estimates of each expert, the dependencies among experts, and potential systematic biases. The relation between the results of the formal methods of this paper and methods used in practice is explored. A series of sensitivity studies provides insights into the significance of the parameters of the model. The methodology is applied to the problem of estimation of seismic fragility curves (i.e., the conditional probability of equipment failure given a seismically induced stress).     

Binary probability maps using a hidden conditional autoregressive Gaussian process with an application to Finnish common toad data

The Finnish common toad data of Heikkinen and Hogmander are reanalysed using an alternative fully Bayesian model that does not require a pseudolikelihood approximation and an alternative prior distribution for the true presence or absence status of toads in each 10 km×10 km square. Markov chain Monte Carlo methods are used to obtain posterior probability estimates of the square-specific presences of the common toad and these are presented as a map. The results are different from those of Heikkinen and Hogmander and we offer an explanation in terms of the prior used for square-specific presence of the toads. We suggest that our approach is more faithful to the data and avoids unnecessary confounding of effects. We demonstrate how to extend our model efficiently with square-specific covariates and illustrate this by introducing deterministic spatial changes.     

On predictive distributions and Bayesian networks

In this paper we are interested in discrete prediction problems for a decision-theoretic setting, where the task is to compute the predictive distribution for a finite set of possible alternatives. This question is first addressed in a general Bayesian framework, where we consider a set of probability distributions defined by some parametric model class. Given a prior distribution on the model parameters and a set of sample data, one possible approach for determining a predictive distribution is to fix the parameters to the instantiation with the maximum a posteriori probability. A more accurate predictive distribution can be obtained by computing the evidence (marginal likelihood), i.e., the integral over all the individual parameter instantiations. As an alternative to these two approaches, we demonstrate how to use Rissanen's new definition of stochastic complexity for determining predictive distributions, and show how the evidence predictive distribution with Jeffrey's prior approaches the new stochastic complexity predictive distribution in the limit with increasing amount of sample data. To compare the alternative approaches in practice, each of the predictive distributions discussed is instantiated in the Bayesian network model family case. In particular, to determine Jeffrey's prior for this model family, we show how to compute the (expected) Fisher information matrix for a fixed but arbitrary Bayesian network structure. In the empirical part of the paper the predictive distributions are compared by using the simple tree-structured Naive Bayes model, which is used in the experiments for computational reasons. The experimentation with several public domain classification datasets suggest that the evidence approach produces the most accurate predictions in the log-score sense. The evidence-based methods are also quite robust in the sense that they predict surprisingly well even when only a small fraction of the full training set is used.     

Penalized Pseudolikelihood Inference in Spatial Interaction Models with Covariates

Given spatially located observed random variables ( x , z = {( x _i , z _i )} _i , we propose a new method for non-parametric estimation of the potential functions of a Markov random field p ( x | z ), based on a roughness penalty approach. The new estimator maximizes the penalized log-pseudolikelihood function and is a natural cubic spline. The calculations involved do not rely on Monte Carlo simulation. We suggest the use of B-splines to stabilize the numerical procedure. An application in Bayesian image reconstruction is described.