What constitutes vulnerable self-esteem? Comparing the prospective effects of low, unstable, and contingent self-esteem on depressive symptoms

A growing body of longitudinal studies suggests that low self-esteem is a risk factor for depression. However, it is unclear whether other characteristics of self-esteem, besides its level, explain incremental or even greater variance in subsequent depression. We examined the prospective effects of self-esteem level, instability (i.e., the degree of variability in self-esteem across short periods), and contingency (i.e., the degree to which self-esteem fluctuates in response to self-relevant events) on depressive symptoms in 1 overarching model, using data from 2 longitudinal studies. In Study 1, 372 adults were assessed at 2 waves over 6 months, including 40 daily diary assessments at Wave 1. In Study 2, 235 young adults were assessed at 2 waves over 6 weeks, including about 6 daily diary assessments at each wave. Self-esteem contingency was measured by self-report and by a statistical index based on the diary data (capturing event-related fluctuations in self-esteem). In both studies self-esteem level, but not self-esteem contingency, predicted subsequent depressive symptoms. Self-esteem instability predicted subsequent depressive symptoms in Study 2 only, with a smaller effect size than self-esteem level. Also, level, instability, and contingency of self-esteem did not interact in the prediction of depressive symptoms. Moreover, the effect of self-esteem level held when controlling for neuroticism and for all other Big Five personality traits. Thus, the findings provide converging evidence for a vulnerability effect of self-esteem level, tentative evidence for a smaller vulnerability effect of self-esteem instability, and no evidence for a vulnerability effect of self-esteem contingency.

Wright’s Version of Critical Realism

N. T. Wright’s research project “Christian Origins and the Question of God” is characterized by its use of the method of critical realism. Now, “critical realism” is a term that has been used in connection with different epistemo-logical positions because the term has been “constantly reinvented.” It is very easy to make up a term when one wants to distinguish oneself from an assumed naïve approach to reality. As has been observed earlier, the use of a distinct term does not necessarily mean the same if used by another author; the context is important. One has to track literal dependencies to evaluate whether continuity with former uses of a term is intended. That is to say, the term “critical realism” has proven to be equivocal, although this has rarely been noticed . This does not mean that taking such a critical realist stance cannot present a decisive advantage over rather unreflective approaches to whatever sort of reality. Nevertheless, philosophically it can probably only be a start. The purpose of this contribution to this compendium will be to analyze the content claims and the status of N. T. Wright’s critical realism in these regards, with a special emphasis on Paul and the Faithfulness of God, of course.

Building the View Graph of a Category by Exploiting Image Realism

We propose a weakly supervised method to arrange images of a given category based on the relative pose between the camera and the object in the scene. Relative poses are points on a sphere centered at the object in a given canonical pose, which we call object viewpoints. Our method builds a graph on this sphere by assigning images with similar viewpoint to the same node and by connecting nodes if they are related by a small rotation. The key idea is to exploit a large unlabeled dataset to validate the likelihood of dominant 3D planes of the object geometry. A number of 3D plane hypotheses are evaluated by applying small 3D rotations to each hypothesis and by measuring how well the deformed images match other images in the dataset. Correct hypotheses will result in deformed images that correspond to plausible views of the object, and thus will likely match well other images in the same category. The identified 3D planes are then used to compute affinities between images related by a change of viewpoint. We then use the affinities to build a view graph via a greedy method and the maximum spanning tree.