新经济增长理论对我国人力资本发展的借鉴

新经济增长理论为人力资本理论注入了新的内容,为各国如何利用人力资本发展经济提供了理论指导。分析了新经济增长理论中有关人力资本的理论,以此对我国经济和人力资本的发展提出了一些借鉴;收益递增模型。     

A motivational model of rural students' intentions to persist in, versus drop out of, high school.

Using self-determination theory, the authors tested a motivational model to explain the conditions under which rural students formulate their intentions to persist in, versus drop out of, high school. The model argues that motivational variables underlie students' intentions to drop out and that students' motivation can be either supported in the classroom by autonomy-supportive teachers or frustrated by controlling teachers. LISREL analyses of questionnaire data from 483 rural high school students showed that the provision of autonomy support within classrooms predicted students' self-determined motivation and perceived competence. These motivational resources, in turn, predicted students' intentions to persist, versus drop out, and they did so even after controlling for the effect of achievement. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Big-Fish--Little-Pond effect on academic self-concept: A cross-cultural (26-country) test of the negative effects of academically selective schools.

Academically selective schools are intended to affect academic self-concept positively, but theoretical and empirical research demonstrates that the effects are negative. The big-fish--little-pond effect (BFLPE), an application of social comparison theory to educational settings, posits that a student will have a lower academic self-concept in an academically selective school than in a nonselective school. This study, the largest cross-cultural study of the BFLPE ever undertaken, tested theoretical predictions for nationally representative samples of approximately 4,000 15-year-olds from each of 26 countries (N=103,558) who completed the same self-concept instrument and achievement tests. Consistent with the BFLPE, the effects of school-average achievement were negative in all 26 countries (M beta=-.20, SD=.08), demonstrating the BFLPE's cross-cultural generalizability. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Information-Based Evaluation Criterion for Classifier's Performance

In the past few years many systems for learning decision rules from examples were developed. As different systems allow different types of answers when classifying new instances, it is difficult to appropriately evaluate the systems' classification power in comparison with other classification systems or in comparison with human experts. Classification accuracy is usually used as a measure of classification performance. This measure is, however, known to have several defects. A fair evaluation criterion should exclude the influence of the class probabilities which may enable a completely uninformed classifier to trivially achieve high classification accuracy. In this paper a method for evaluating the information score of a classifier's answers is proposed. It excludes the influence of prior probabilities, deals with various types of imperfect or probabilistic answers and can be used also for comparing the performance in different domains.     

Consolidation of Long-Term Memory: Evidence and Alternatives.

Memory loss in retrograde amnesia has long been held to be larger for recent periods than for remote periods, a pattern usually referred to as the Ribot gradient. One explanation for this gradient is consolidation of long-term memories. Several computational models of such a process have shown how consolidation can explain characteristics of amnesia, but they have not elucidated how consolidation must be envisaged. Here findings are reviewed that shed light on how consolidation may be implemented in the brain. Moreover, consolidation is contrasted with alternative theories of the Ribot gradient. Consolidation theory, multiple trace theory, and semantization can all handle some findings well but not others. Conclusive evidence for or against consolidation thus remains to be found. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Dynamique motivationnelle de l'epuisement et du bien-etre chez des enseignants africains.

This study examines the links between antecedents and consequences of Gabonese teachers' (N= 152) work motivation. The theoretical underpinnings are based on the Motivational Model of Job Burnout (MMJB) which is grounded in Self-Determination Theory (SDT) and states that the more supervisors satisfy an employee's need for self-determination, competence, and relatedness, the more self-determined is the latter's work motivation. The level of self-determined motivation has an impact on job satisfaction, which in turn determines life satisfaction. Higher levels of self-determined motivation also buffer emotional exhaustion and psychological distress. The model was confirmed via structural equation modeling and provides support for the validity and generalizability of the MMJB and SDT. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Dynamic stiffness vibration analysis using a high‐order beam model

This work presents the derivation of the exact dynamic stiffness matrix for a high‐order beam element. The terms are found directly from the solutions of the differential equations that describe the deformations of the cross‐section according to the high‐order theory, which include cubic variation of the axial displacements over the cross‐section of the beam. The model has six degrees of freedom at the two ends, one transverse displacement and two rotations, and the end forces are a shear force and two end moments. Using the dynamic stiffness matrix exact vibration frequencies for beams with various combinations of boundary conditions are tabulated and compared with results from the Bernoulli–Euler and Timoshenko beam models. Copyright © 2003 John Wiley & Sons, Ltd.     

Many-Body Terms in van der Waals Cohesion Energy of Nanotubes

We have developed a model for the calculation of van der Waals force for layered systems with axial symmetry. Our result can be applied to compute the cohesion of a carbon nanotube to a substrate, the cohesion between nanotubes, and between shells of multiwall nanotubes. We have obtained unusal power laws for the distance dependence of the many-body van der Waals potential.     

Web-based systems design: a study of contemporary practices and an explanatory framework based on “method-in-action”

This paper reports the findings of a detailed study of Web-based systems design (WBSD) practices in Ireland based on data collected over a 3-year period (2002–2005), the objectives of which were to (1) contribute towards a richer understanding of the current “real-world” context of WBSD by characterising the profile of a typical project (team size, timeframe, nature of requirements, etc.) and identifying the key challenges, constraints, and imperatives (i.e. “mediating factors”) faced by Web-based system designers, and (2) understand how those contextual parameters and mediating factors influence the activity of WBSD as regards the selection and enactment of whatever design practices are therefore engaged (i.e. the use of methods, procedures, etc.). Data was gathered through a survey which yielded 165 usable responses, and later through a series of semi-structured qualitative interviews. Using grounded theory, an explanatory conceptual framework is derived, based on an extension of the “method-in-action” model, the application of which to WBSD has not been previously investigated in depth. It is proposed that this framework of WBSD issues is valuable in a number of ways to educators, researchers, practitioners, and method engineers.     

Multiagent learning using a variable learning rate

Learning to act in a multiagent environment is a difficult problem since the normal definition of an optimal policy no longer applies. The optimal policy at any moment depends on the policies of the other agents. This creates a situation of learning a moving target. Previous learning algorithms have one of two shortcomings depending on their approach. They either converge to a policy that may not be optimal against the specific opponents' policies, or they may not converge at all. In this article we examine this learning problem in the framework of stochastic games. We look at a number of previous learning algorithms showing how they fail at one of the above criteria. We then contribute a new reinforcement learning technique using a variable learning rate to overcome these shortcomings. Specifically, we introduce the WoLF principle, “Win or Learn Fast”, for varying the learning rate. We examine this technique theoretically, proving convergence in self-play on a restricted class of iterated matrix games. We also present empirical results on a variety of more general stochastic games, in situations of self-play and otherwise, demonstrating the wide applicability of this method.