Public policies of social mixing have been enacted as the reversal of what segregation and concentrated poverty are presumed to have produced: intensified social problems (i.e., “neighborhood effects”). In addition, the pervasive discourses of diversity have provided more support for the idea of social mixing. Studies on planned and unplanned diverse neighborhoods have shown how certain diverse patterns can emerge and endure over time. Yet these studies have failed to explain how such demographic diversity becomes integration. In this article, I draw on a multidimensional perspective of socio-spatial integration to present a qualitative case study of the Cabrini Green/Near North area in Chicago—a neighborhood with a long history of segregation and recent socially engineered diversity. The case shows how contentious this new coexistence has been, and how segregation has been shifting its mechanisms of enforcement from housing to other spheres of life. I conclude with reflections on four dimensions of socio-spatial integration, and on the troubling policy and theoretical implications of the “social mix” paradigm. 相似文献
Abstract. Cymodocea nodosa is a relatively small seagrass species which is common in the Mediterranean. An intensive survey on its growth and production was carried out in a dense, monospecific stand located in a semi-estuarine embayment. Data on leaf appearance and growth, shoot recruitment and death, rhizome growth, above- and belowground biomass, and nutrient content in the different parts of the plant were obtained over 2 years. All these variables showed a clear seasonality. In general, maximum growth and production occurred in early summer (July), and maximum biomass was reached between July and September. Biomass, shoot density, growth and production showed clear minima in winter. 相似文献
Two different Cymodocea nodosa (Ucria) Ascherson beds growing in mining-contaminated sediments were compared with two reference beds in the Mar Menor coastal lagoon. The accumulation of Zn, Pb and Cd in different fractions of the plant, the sediment parameters that regulate the availability of metals, the seabed structure and dynamics of each seagrass bed and its associated macroinvertebrate community were studied. C. nodosa accumulates metals from the sediments and reflects their bioavailability for this seagrass. At each station, the metal content of the rhizomes was lower than that of leaves and roots. The concentration of acid-volatile sulfides does not seem to influence the availability of metals to the seagrass, possibly due to oxygen transport to underground tissues. The highest metal concentration in all the contaminated stations was found in the leaf-biofilm, due to the formation of complexes between metals and the extracellular polymeric substances that form the biofilm. All the seagrass beds were seen to be undergoing expansion, those growing in contaminated sediments accumulating great quantities of metals and showing highest photosynthetic leaf surface area and highest leaf biomass. However, these structural parameters were not seen to be responsible for the differences in the faunal composition observed between contaminated and reference beds. Moreover, the multivariate analysis identified the metal content of leaves, biofilm and sediments as important variables that may be responsible for these differences in faunal composition. In this study we have demonstrated that both the seagrass C. nodosa and the biofilm on the plant leaves may be used as environmental tools in the Mar Menor lagoon. The former is an useful indicator of sediment contamination, whereas the latter seems to be a good sentinel of water quality. 相似文献
Eutrophication is one of the most relevant man‐induced changes occurring in coastal waters. The identification and assessment of specific responses to eutrophication in seagrasses can provide a useful tool for the detection of changes in the water quality in coastal zones, given the wide range of distribution of these organisms. In this study, we combine a correlational (across‐sites comparison) and a manipulative (fertilization experiment) approach to evaluate the usefulness and potential of alkaline phosphatase activity (APA) in the endemic Mediterranean seagrass Posidonia oceanica as an eutrophication biomarker. Our results showed that APA decreases promptly following nutrient additions, the response being maintained except during the winter period. APA also varies across natural meadows under different levels of nutrient discharges at scales relevant for monitoring purposes. AP activity seems to be an optimal ‘physiological biomarker’ that responds promptly and reliably to a pulse of eutrophication exposure. However, other considerations, such as the seasonality (the response disappears in winter), suggest its use with some caution and, as far as possible, as a complement of other bio‐indicators. 相似文献
Nuclear waste from thermal plants poses a lasting risk to the biosphere because of its long radioactive life. The planned definitive storage place for it is in deeply buried repositories. Such repositories would need to be both impermeable to water, and plastic during deformation, in order to avoid the formation of cracks that may allow water in. One of the clay minerals, smectite, has these two properties and is an ideal candidate as a sealing material or even host rock for nuclear waste repositories. The chemical stability of smectite in the repository environment is sufficient to maintain good sealing properties during the active life of the relevant radionuclides. 相似文献
Traditionally, earthquake impact assessments have been made via fieldwork by non-governmental organisations (NGO's) sponsored data collection; however, this approach is time-consuming, expensive and often limited. Recently, social media (SM) has become a valuable tool for quickly collecting large amounts of first-hand data after a disaster and shows great potential for decision-making. Nevertheless, extracting meaningful information from SM is an ongoing area of research. This paper tests the accuracy of the pre-trained sentiment analysis (SA) model developed by the no-code machine learning platform MonkeyLearn using the text data related to the emergency response and early recovery phase of the three major earthquakes that struck Albania on the 26th November 2019. These events caused 51 deaths, 3000 injuries and extensive damage. We obtained 695 tweets with the hashtags: #Albania #AlbanianEarthquake, and #albanianearthquake from the 26th November 2019 to the 3rd February 2020. We used these data to test the accuracy of the pre-trained SA classification model developed by MonkeyLearn to identify polarity in text data. This test explores the feasibility to automate the classification process to extract meaningful information from text data from SM in real-time in the future. We tested the no-code machine learning platform's performance using a confusion matrix. We obtained an overall accuracy (ACC) of 63% and a misclassification rate of 37%. We conclude that the ACC of the unsupervised classification is sufficient for a preliminary assessment, but further research is needed to determine if the accuracy is improved by customising the training model of the machine learning platform.