How Long Do African Americans Stay in High-Poverty Neighborhoods? An Analysis of Spells

Discussions of high-poverty neighborhoods often assume that their residents are a distinct population trapped in poor neighborhoods for long durations. This paper examines this claim by calculating the first estimates of duration of residence in high-poverty neighborhoods for the African-American population. Using data from the Panel Study of Income Dynamics matched to census tract data and a model of movement among neighborhood types adopted from Bane and Ellwood (1983, 1986) and McGinnis (1968), I derive measures of the duration of stays in high-poverty neighborhoods. A large share of the black population will experience a short spell of residence in an extremely poor neighborhood at some time over a 10-year period. Many of the residents of nonpoor and poor neighborhoods at a point in time, however, will be there for long spells. Among poor African Americans, reentry to high-poverty neighborhoods following an exit is common. Patterns of stays in high-poverty neighborhoods are more complex and heterogeneous than usually supposed.

Socio-Economic Segregation in Large Cities in France and the United States

This working paper calculates measures of the level of socioeconomic segregation in large metropolitan areas (cities and their surrounding suburbs) in the United States and France. The authors define “large” metropolitan areas as city-suburb combinations with a population of greater than one million. They use tract data from the American Community Survey (2006-2010) and data from the French Census of 2008 and the French Ministry of Finance. The results reveal a significantly higher level of socioeconomic segregation in large American than in French cities. American cities are more segregated than French cities on all three measures considered here: income, employment, and education. This finding holds with measures that account for different distributions of income, unemployment, and education across the two countries. The researchers also find (1) a strong pattern of low-income neighborhoods in central cities, and high-income neighborhoods in suburbs in the United States, but not in France; (2) that high-income persons are the most segregated group in both countries; (3) that the shares of neighborhood income differences that can be explained by neighborhood race-ethnic composition are similar in France and the United States, suggesting that racial segregation cannot account for much of the higher level of U.S. socioeconomic segregation

Alzheimer’s in the Geriatric Population

Because there is a critical need for nurses in geriatric healthcare facilities, this study examines two central questions: 1) How does under-staffing affect Alzheimer’s patients and their mental health? and 2) How do we raise awareness about neglect and under-staffing within health care facilities? This study includes a special focus on youth perceptions of the geriatric population

On The Irrelevance Of Transformational Grammar To Second Language Pedagogy

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/98173/1/j.1467-1770.1969.tb00467.x.pd

Global Organization of the Lexicon

The lexicon consists of a set of word meanings and their semantic relationships. A systematic representation of the English lexicon based in psycholinguistic considerations has been put together in the database Wordnet in a long-term collaborative effort1. We present here a quantitative study of the graph structure of Wordnet in order to understand the global organization of the lexicon. We find that semantic links follow power-law, scale-invariant behaviors typical of self-organizing networks. Polysemy, the ambiguity of an individual word, can act as a link in the semantic network, relating the different meanings of a common word. Inclusion of polysemous links has a profound impact in the organization of the semantic graph, converting it into a small world, with clusters of high traffic (hubs) representing abstract concepts. Our results show that polysemy organizes the semantic graph in a compact and categorical representation, and thus may explain the ubiquity of polysemy across languages

Enrichment and ranking of the YouTube tag space and integration with the Linked Data cloud

The increase of personal digital cameras with video functionality and video-enabled camera phones has increased the amount of user-generated videos on the Web. People are spending more and more time viewing online videos as a major source of entertainment and “infotainment”. Social websites allow users to assign shared free-form tags to user-generated multimedia resources, thus generating annotations for objects with a minimum amount of effort. Tagging allows communities to organise their multimedia items into browseable sets, but these tags may be poorly chosen and related tags may be omitted. Current techniques to retrieve, integrate and present this media to users are deficient and could do with improvement. In this paper, we describe a framework for semantic enrichment, ranking and integration of web video tags using Semantic Web technologies. Semantic enrichment of folksonomies can bridge the gap between the uncontrolled and flat structures typically found in user-generated content and structures provided by the Semantic Web. The enhancement of tag spaces with semantics has been accomplished through two major tasks: a tag space expansion and ranking step; and through concept matching and integration with the Linked Data cloud. We have explored social, temporal and spatial contexts to enrich and extend the existing tag space. The resulting semantic tag space is modelled via a local graph based on co-occurrence distances for ranking. A ranked tag list is mapped and integrated with the Linked Data cloud through the DBpedia resource repository. Multi-dimensional context filtering for tag expansion means that tag ranking is much easier and it provides less ambiguous tag to concept matching

Semantic memory redux: an experimental test of hierarchical category representation

Four experiments investigated the classic issue in semantic memory of whether people organize categorical information in hierarchies and use inference to retrieve information from them, as proposed by Collins & Quillian (1969). Past evidence has focused on RT to confirm sentences such as “All birds are animals” or “Canaries breathe.” However, confounding variables such as familiarity and associations between the terms have led to contradictory results. Our experiments avoided such problems by teaching subjects novel materials. Experiment 1 tested an implicit hierarchical structure in the features of a set of studied objects (e.g., all brown objects were large). Experiment 2 taught subjects nested categories of artificial bugs. In Experiment 3, subjects learned a tree structure of novel category hierarchies. In all three, the results differed from the predictions of the hierarchical inference model. In Experiment 4, subjects learned a hierarchy by means of paired associates of novel category names. Here we finally found the RT signature of hierarchical inference. We conclude that it is possible to store information in a hierarchy and retrieve it via inference, but it is difficult and avoided whenever possible. The results are more consistent with feature comparison models than hierarchical models of semantic memory

Decreased symptoms of depression after mindfulness-based stress reduction: potential moderating effects of religiosity, spirituality, trait mindfulness, sex, and age

Objective: mindfulness-based stress reduction (MBSR) is a secular meditation training program that reduces depressive symptoms. Little is known, however, about the degree to which a participant's spiritual and religious background, or other demographic characteristics associated with risk for depression, may affect the effectiveness of MBSR. Therefore, this study tested whether individual differences in religiosity, spirituality, motivation for spiritual growth, trait mindfulness, sex, and age affect MBSR effectiveness.Methods: as part of an open trial, multiple regression was used to analyze variation in depressive symptom outcomes among 322 adults who enrolled in an 8-week, community-based MBSR program.Results: as hypothesized, depressive symptom severity decreased significantly in the full study sample (d=0.57; p<0.01). After adjustment for baseline symptom severity, moderation analyses revealed no significant differences in the change in depressive symptoms following MBSR as a function of spirituality, religiosity, trait mindfulness, or demographic variables. Paired t tests found consistent, statistically significant (p<0.01) reductions in depressive symptoms across all subgroups by religious affiliation, intention for spiritual growth, sex, and baseline symptom severity. After adjustment for baseline symptom scores, age, sex, and religious affiliation, a significant proportion of variance in post-MBSR depressive symptoms was uniquely explained by changes in both spirituality (?=?0.15; p=0.006) and mindfulness (?=?0.17; p<0.001).Conclusions: these findings suggest that MBSR, a secular meditation training program, is associated with improved depressive symptoms regardless of affiliation with a religion, sense of spirituality, trait level of mindfulness before MBSR training, sex, or age. Increases in both mindfulness and daily spiritual experiences uniquely explained improvement in depressive symptom

Dynamics of Transformation from Segregation to Mixed Wealth Cities

We model the dynamics of the Schelling model for agents described simply by a continuously distributed variable - wealth. Agents move to neighborhoods where their wealth is not lesser than that of some proportion of their neighbors, the threshold level. As in the case of the classic Schelling model where segregation obtains between two races, we find here that wealth-based segregation occurs and persists. However, introducing uncertainty into the decision to move - that is, with some probability, if agents are allowed to move even though the threshold level condition is contravened - we find that even for small proportions of such disallowed moves, the dynamics no longer yield segregation but instead sharply transition into a persistent mixed wealth distribution. We investigate the nature of this sharp transformation between segregated and mixed states, and find that it is because of a non-linear relationship between allowed moves and disallowed moves. For small increases in disallowed moves, there is a rapid corresponding increase in allowed moves, but this tapers off as the fraction of disallowed moves increase further and finally settles at a stable value, remaining invariant to any further increase in disallowed moves. It is the overall effect of the dynamics in the initial region (with small numbers of disallowed moves) that shifts the system away from a state of segregation rapidly to a mixed wealth state. The contravention of the tolerance condition could be interpreted as public policy interventions like minimal levels of social housing or housing benefit transfers to poorer households. Our finding therefore suggests that it might require only very limited levels of such public intervention - just sufficient to enable a small fraction of disallowed moves, because the dynamics generated by such moves could spur the transformation from a segregated to mixed equilibrium.Comment: 12 pages, 7 figure

The algebra of lexical semantics

Abstract. The current generative theory of the lexicon relies primar-ily on tools from formal language theory and mathematical logic. Here we describe how a different formal apparatus, taken from algebra and automata theory, resolves many of the known problems with the gener-ative lexicon. We develop a finite state theory of word meaning based on machines in the sense of Eilenberg [11], a formalism capable of de-scribing discrepancies between syntactic type (lexical category) and se-mantic type (number of arguments). This mechanism is compared both to the standard linguistic approaches and to the formalisms developed in AI/KR. 1 Problem Statement In developing a formal theory of lexicography our starting point will be the informal practice of lexicography, rather than the more immediately related for-mal theories of Artificial Intelligence (AI) and Knowledge Representation (KR). Lexicography is a relatively mature field, with centuries of work experience an