Probability Disassembled

While there is no universal logic of induction, the probabilitycalculus succeeds as a logic of induction in many contexts throughits use of several notions concerning inductive inference. Theyinclude Addition, through which low probabilities representdisbelief as opposed to ignorance; and Bayes property, whichcommits the calculus to a ‘refute and rescale’ dynamicsfor incorporating new evidence. These notions are independentand it is urged that they be employed selectively accordingto needs of the problem at hand. It is shown that neither isadapted to inductive inference concerning some indeterministicsystems.

1 Introduction

2 Failure of demonstrations of universality

2.1 Working backwards

2.2 The surface logic

3 Framework

3.1 The properties

3.2 Boundaries

3.2.1 Universalcomparability

3.2.2 Transitivity

3.2.3 Monotonicity

4 Addition

4.1 The property: disbelief versus ignorance

4.2Boundaries

5 Bayes property

5.1 The property

5.2 Bayes' theorem

5.3Boundaries

5.3.1 Dogmatism of the priors

5.3.2 Impossibilityof prior ignorance

5.3.3 Accommodation of virtues

6Real values

7 Sufficiency and independence

8 Illustrations

8.1 All properties retained

8.2 Bayes propertyonly retained

8.3 Induction without additivity and Bayes property

9Conclusion

     

秦始皇兵马俑博物馆室内含硫颗粒物的SEM-EDX研究

本文对秦始皇兵马俑博物馆室内采集的长短期降尘、大气悬浮颗粒物和彩绘漆层进行了SEM-EDX研究，在大气悬浮颗粒、短期和长期降尘中，含硫颗粒的粒径分别为0.9～22.8μm、1.0～29.0μm和1.2～37.7μm，多为石膏与粘土或石英以内部混合的状态存在。降尘中的附着型絮状硫酸钙多于大气悬浮颗粒物中，显示降尘和大气中的二氧化硫发生了累进的化学反应。彩绘漆层表面的坑和裂隙附近观察到原位生长的硫酸钙晶体，显示漆层材料或降尘颗粒物与大气二氧化硫之间的酸化学反应，可能是彩绘漆层表面受侵蚀形成微小坑和裂隙的原因之一。     

基于SSM分折的科研评价系统探讨

科技评价体系是一个复杂的,多目标评价体系。当今科技评价已逐渐从强调科研产出,转向同时关注资源的使用效率,注重科技创新能力的提升和对国民经济发展的贡献。如何建立一个客观、公正、健全、规范的,且具备可操作性的科学评价体系引起了政府和科技界的广泛关注。本文以SSM系统分析为基础,从3E出发探讨了评价指标体系的构建和评价系统的设计。基于系统分析基础上形成的指标体系,既考虑了直接的产出,又分析了内部运行机制,形成的指标体系相对更全面、完整,在此基础上针对具体评价内容选择适宜的评价方法,可定量或定性,软硬结合,具有透明性和延展性,同时易与决策管理部门交流。     

Factors influencing early adolescents’ mathematics achievement: High-quality teaching rather than relationships

This study used data from the Assessment Tools for Teaching and Learning project, which involved data on the academic performance of more than 90,000 New Zealand students in six subjects (i.e. reading, writing and mathematics in two languages). Two sub-samples of this dataset were included for detailed re-analysis to test the general applicability of the Australian model of Quality of School Life (Ainley et al. 1986) in the New Zealand context. The first sample comprised 336 year 8 students from elementary schools and the second sample consisted of 272 year 10 students from high schools. Furthermore, two structural equation models were developed and tested, expressing relationships between students’ quality of school life perceptions, students’ attitudes to mathematics, and their effects on mathematics achievement. The quality of school life questionnaires scales [Ainley and Bourke, in Res Pap Educ 7(2):107–128, 1992] were used as indicators of students’ perceptions regarding learning, teachers and peer relationships. The model proposed that perceived quality of school life would affect students’ attitudes of liking and confidence in mathematics, which would in turn affect their academic performance. After controlling for other variables in the model, students’ perception about their self-efficacy to learn mathematics was more directly related to outcomes than to perceptions of teacher quality or peer involvement. Data analyses revealed no apparent relationships of these factors to mathematics achievement. Moreover, results for both samples led to the conclusion that the perceived quality of learning is connected with ‘confidence in’ and ‘liking mathematics’, which in turn predict students’ mathematics achievement.