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Formulated in the 1980s and gaining prominence in the early 1990s, by the end of that decade ecofeminism was critiqued as essentialist and effectively discarded. Fearing their scholarship would be contaminated by association with the term "ecofeminism," feminists working on the intersections of feminism and environmentalism thought it better to rename their approach. Thirty years later, current developments in allegedly new fields such as animal studies and naturalized epistemology are "discovering" theoretical perspectives on interspecies relations and standpoint theory that were developed by feminists and ecofeminists decades ago. What have we lost by jettisoning these earlier feminist and ecofeminist bodies of knowledge? Are there features of ecofeminism that can helpfully be retrieved, restoring an intellectual and activist history, and enriching current theorizing and activisms? By examining the historical foundations of ecofeminism from the 1980s onward, this article uncovers the roots of the antifeminist backlash against ecofeminism in the 1990s, peeling back the layers of feminist and environmentalist resistance to ecofeminism's analyses of the connections among racism, sexism, classism, colonialism, speciesism, and the environment. Recuperating ecofeminist insights of the past thirty years provides feminist foundations for current liberatory theories and activisms.
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This article presents the first meta-analysis of experimental research on rejection, sampling 88 studies. The results are consistent with a needs account, which states that rejection frustrates basic psychological needs, but not with a numbness account, which states that rejection causes physical and emotional numbness. Rejection moderately lowers mood (d = -0.50) and self-esteem (d = -0.70), but does not decrease arousal or flatten affect. Both belonging (d = 0.69) and control (d = 1.16) are frustrated by rejection. Aggressive responses to rejection, considered paradoxical by some, appear to be due to attempts to gain control; measures that contrast belonging and control (d = -1.17) cause antisocial responding, whereas measures that do not allow for control to be restored cause prosocial responding (d = 1.21). These findings suggest that rejection makes individuals feel bad-ready to act to restore control or belonging-and that they will prioritize restoring control even if it requires being antisocial.
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The purposes of this study were to examine academically relevant characteristics of different sociometric status groups and to learn about the academic orientations of behavioral subgroups of rejected children. Results from a sample of 423 sixth and seventh graders (ages 11-13) suggested that sociometrically neglected children have quite positive academic profiles. When compared with average status children, these students reported higher levels of motivation, were described by teachers as more self-regulated learners, as more prosocial and compliant, and as being better liked by teachers. Analyses of two behavioral subgroups of rejected children indicated that aggressive-rejected but not submissive-rejected children have problematic academic profiles. Relations of neglected and aggressive-rejected status to academic adjustment in young adolescents' lives is discussed.
Journal Article A sequentially rejective test procedure based on a modified Bonferroni inequality Get access DROR M. ROM DROR M. ROM Rorer Central Research800 Business Center Drive, Horsham, Pennsylvania 19044, U.S.A. Search for other works by this author on: Oxford Academic Google Scholar Biometrika, Volume 77, Issue 3, September 1990, Pages 663–665, https://doi.org/10.1093/biomet/77.3.663 Published: 01 September 1990 Article history Received: 01 February 1990 Revision received: 01 March 1990 Published: 01 September 1990
For clinical trials with multiple treatment arms or endpoints a variety of sequentially rejective, weighted Bonferroni-type tests have been proposed, such as gatekeeping procedures, fixed sequence tests, and fallback procedures. They allow to map the difference in importance as well as the relationship between the various research questions onto an adequate multiple test procedure. Since these procedures rely on the closed test principle, they usually require the explicit specification of a large number of intersection hypotheses tests. The underlying test strategy may therefore be difficult to communicate. We propose a simple iterative graphical approach to construct and perform such Bonferroni-type tests. The resulting multiple test procedures are represented by directed, weighted graphs, where each node corresponds to an elementary hypothesis, together with a simple algorithm to generate such graphs while sequentially testing the individual hypotheses. The approach is illustrated with the visualization of several common gatekeeping strategies. A case study is used to illustrate how the methods from this article can be used to tailor a multiple test procedure to given study objectives.
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The authors asked the world's leading economists to describe instances in which journals rejected their articles. More than sixty essays, by a broadly diverse group that includes fifteen Nobel Prize winners, indicate that most have suffered publication rejection, often frequently. Indeed, journals have rejected many papers that later became classics. The authors discuss the prize-winners’ experiences, other notable cases, and rejections by John Maynard Keynes when he edited the Economic Journal. Finally, they search in economists’ almost universal experience of rejection for patterns and lessons about the publication process.
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We describe a method to produce a network where current methods such as DeepFool have great difficulty producing adversarial samples. Our construction suggests some insights into how deep networks work. We provide a reasonable analyses that our construction is difficult to defeat, and show experimentally that our method is hard to defeat with both Type I and Type II attacks using several standard networks and datasets. This SafetyNet architecture is used to an important and novel application SceneProof, which can reliably detect whether an image is a picture of a real scene or not. SceneProof applies to images captured with depth maps (RGBD images) and checks if a pair of image and depth map is consistent. It relies on the relative difficulty of producing naturalistic depth maps for images in post processing. We demonstrate that our SafetyNet is robust to adversarial examples built from currently known attacking approaches.
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This article examines the news behaviors and attitudes of teenagers, an understudied demographic in the research on youth and news media. Based on interviews with 61 racially diverse high school students, it discusses how adolescents become informed about current events and why they prefer certain news formats to others. The results reveal changing ways news information is being accessed, new attitudes about what it means to be informed, and a youth preference for opinionated rather than objective news. This does not indicate that young people disregard the basic ideals of professional journalism but, rather, that they desire more authentic renderings of them.
Social exclusion evokes powerful motivations and emotions. The present studies examined how these motivations and emotions might differ following exclusion that is explicit, active, and direct (i.e., when one is rejected) versus implicit, passive, and indirect (i.e., when one is ignored). It was hypothesized that being rejected should produce a sense of social loss and lead to more prevention-focused responses, including withdrawal from social contact, thoughts about actions one should not have taken, and increased feelings of agitation. In contrast, being ignored should produce a sense of failure to achieve social gain and lead to more promotion-focused responses, including reengagement in social contact, thoughts about actions one should have taken, and increased feelings of dejection. These hypotheses were supported across 4 studies in which people recalled or underwent experiences of being rejected or ignored. Past research on active versus passive exclusion is reexamined and found to be consistent with these hypotheses as well.
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In [3] the author established necessary and sufficient conditions for asymptotic normality of estimates based on simple random sampling without replacement from a finite population, and thus solved a comparatively old problem initiated by W. G. Madow [8]. The solution was obtained by approximating simple random sampling by so called Poisson sampling, which may be decomposed into independent subexperiments, each associated with a single unit in the population. In the present paper the same method is used for deriving asymptotic normality conditions for a special kind of sampling with varying probabilities called here rejective sampling. Rejective sampling may be realized by $n$ independent draws of one unit with fixed probabilities, generally varying from unit to unit, given the condition that samples in which all units are not distinct are rejected. If the drawing probabilities are constant, rejective sampling coincides, with simple random sampling without replacement, and so the present paper is a generalization of [3]. Basic facts about rejective sampling are exposed in Section 2. To obtain more refined results, Poisson sampling is introduced and analyzed (Section 3) and then related to rejective sampling (Section 4). Next three sections deal with probabilities of inclusion, variance formulas and asymptotic normality of estimators for rejective sampling. In Section 8 asymptotic formulas are tested numerically and applications to sample surveys are indicated. The paper is concluded by short-cuts in practical performance of rejective sampling. The readers interested in applications only may concentrate upon Sections 1, 8 and 9. Those interested in the theory of mean values and variances only, may omit Lemma 4.3 and Section 7.
Abstract. We consider the problem of binary classification where the classifier can, for a particular cost, choose not to classify an observation. Just as in the conventional classification problem, minimization of the sample average of the cost is a difficult optimization problem. As an alternative, we propose the optimization of a certain convex loss function φ, analogous to the hinge loss used in support vector machines (SVMs). Its convexity ensures that the sample average of this surrogate loss can be efficiently minimized. We study its statistical properties. We show that minimizing the expected surrogate loss—the φ-risk— also minimizes the risk. We also study the rate at which the φ-risk approaches its minimum value. We show that fast rates are possible when the conditional probability P(Y = 1|X) is unlikely to be close to certain critical values. Key words and phrases: Bayes classifiers; classification; convex surrogate loss; empirical risk minimization; hinge loss; large margin classifiers; margin condition; reject option; support vector machines. MSC 2000: Primary 62C05; secondary 62G05, 62G08 1.
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