Feeling differently together: The It Gets Better Project as an unlikely intimate public of LGBT peers

This article examines peer participation in the It Gets Better Project, a YouTube-based platform which addresses precarious LGBT youth by offering moral support. The paper asks who engages with the project, and why particularly peers participate by recording and uploading a personal video. Contrary to this prevalence of peers, the article first shows how the project's architecture actually diverts attention from peer participation to a few featured videos, thereby shifting the focus onto celebrity participation. The article then proposes the project as an “intimate public” (Berlant, 2008) of shared feelings of difference, which connects the individual and dispersed private, amateur spaces with an indefinite affective space of public intimacy, grounded materially in and mediated by the space of the internet. Building on queer critique, the article argues that the project's success amongst peers can be understood in its capacity to circulate and share emotional knowledge of feeling differently together with others, even despite its internal dominant structural and normative orientation.     

Robust interactive decision-analysis (RID): Behavioral results and implications

We have proposed a novel interactive procedure for performing decision analysis, called Robust Interactive Decision Analysis (RID), which permits a decision maker (DM) to voluntarily and interactively express strong (viz, sure) binary preferences for actions, partial decision functions, and full decision functions, and only imprecise probability and utility function assessments. These serve as INPUTS TO operators to prune the state probability space and decision space until an optimal choice strategy is obtained. The viability of the RID approach depends on a DM's ability to provide such information consistently and meaningfully. On a limited scale we experimentally investigate the behavioral implications of the RID method in order to ascertain its potential operational feasibility and viability. More specifically, we examine whether a DM can (1) express strong preferences between pairs of vectors of unconditional and conditional payoffs or utilities consistently; (2) provide imprecise (ordinal and interval) state probabilities that are individually as well as mutually consistent with the state probabilities imputed from the expressed strong preferences. The results show that a DM can provide strong individually and mutually consistent preference and ordinal probability information. Moreover, most individuals also appear to be able to provide interval probabilities that are individually and mutually consistent with their strong preference inputs. However, the several violations observed, our small sample size, and the limited scope of our investigation suggest that further experimentation is needed to determine whether and/or how such inputs should be elicited. Overall, the results indicate that the RID method is behaviorally viable.     

Saying and doing: A contingency-space analysis

Correspondences between verbal responding (saying) and nonverbal responding (doing) may be organized in terms of the classes of verbal/nonverbal relations into which particular instances of verbal/nonverbal response sequences can enter. Contingency spaces, which display relations among events in terms of the probability of one event given or not given another, have been useful in analyses of nonverbal behavior. We derive a taxonomy of verbal/nonverbal behavior relations from a contingency space that takes into account two conditional probabilities: the probability of a nonverbal response given a verbal response and that probability given the absence of the verbal response. For example, positive correspondence may be said to exist as a response class when the probability of doing is high given saying but is otherwise low. Criteria for other generalized classes, including negative correspondence, follow from this analysis.     

RESCUE AND RECOVERY AS A THEOLOGICAL PRINCIPLE,AND A KEY TO MORALITY IN EXTRATERRESTRIAL SPECIES

New theological understanding can emerge with the advancement of scientific knowledge and the use of new concepts, or older concepts in new ways. Here, the authors present a proposal to extend the concept of “rescue and recovery” found in the United Nations Law of the High Seas, off-world and within a broader purview of other intelligent and self-aware species that humans may someday encounter. The notion of a morality that extends to off-world species is not new, but in this analysis, rescue and recovery become an imperative when any intelligent and self-aware species is in harm's way. Rescue and recovery encompass a necessary action component except perhaps in those cases where rescuers are in danger. The authors explore three biblical examples of rescue and recovery to derive a fundamental meaning of the concept.     

计量测验效度的一种方法——测量目标结构空间的实证性因素分析

该文提出了计量测验效度的一种统一方法。首先,根据测量目标由测验编制者提出一组可能的线性拟合模型,拟合模型的集合称作“测量目标结构空间”。然后运用实证性因素分析对各个假设的拟合模型进行多方面的拟合良好性检验。若其中的某一假设模型能最优地拟合预测资料,则该模型中的公共因素的方差总贡献与观测变量的总方差的比就可定义为该测验的效度     

Jungian Constructivism and the Value of Uncertainty

Introducing the basic assumptions of constructivism as a philosophical position, this paper illustrates how Jung's psychology - especially complex, archetype and transcendent function - is consonant with constructivism. Further, the paper explores some clinical implications of constructivism by reviewing the problems of chronic projective-identification in a stalemated analytic case, drawing on the contributions of Winnicott, Ogden and Modell in expanding our understanding of, and facility with, the transcendent function.     

运用规则空间模型识别解题中的认知错误

运用规则空间模型识别学生解题中的认知错误,该模型将认知心理学,项目反应理论和数据库的代数理论相结合,将被试的二值反应模式映射为笛卡尔乘积空间的一组序偶,然后计算被试和错误规则在该空间的代表性位置之间的马氏距离,并通过贝叶斯决策准则划分被试的错误类型,根据６４４名中学生对３０个数学题目的反应,识别出其中８６％的学生可以被划分人１８种认知错误类型中。     

Fitting longitudinal reduced-rank regression models by alternating least squares

An alternating least squares method for iteratively fitting the longitudinal reduced-rank regression model is proposed. The method uses ordinary least squares and majorization substeps to estimate the unknown parameters in the system and measurement equations of the model. In an example with cross-sectional data, it is shown how the results conform closely to results from eigenanalysis. Optimal scaling of nominal and ordinal variables is added in a third substep, and illustrated with two examples involving cross-sectional and longitudinal data.Financial support by the Institute for Traffic Safety Research (SWOV) in Leidschendam, The Netherlands is gratefully acknowledged.