Living wage campaigns and community-based labor movements in the twenty-first century

This thesis explores the causes and consequences of living wage campaigns in the United States. Continuing shifts in U.S. ethnic politics engender a variety of opportunities for these campaigns to ultimately enhance the welfare of the working class and minorities. Researchers have concluded that living wage campaigns acquire all basic components of social movements, but have not developed a model for explaining variation in the causes and consequences of living wage campaigns. We present a model in which the following are key elements: the strength of both the social movement coalition, the government/business opposition, the mode of coalition-opposition interaction, media portrayal, community support, and the degree of policy and institutional transformation. Analysis of case studies of the living wage campaigns in Baltimore, Chicago, Los Angeles, and Miami between 1994 and 1999 is based on information obtained in articles in Labor Notes, Labor Studies Journal, and New Labor Forum. Coalition-opposition interaction was more likely to be characterized by dialogue than conflict when the coalition before the campaign and the opposition before the campaign were both strong or both weak. The degree of policy and institution transformation was greater when there was more support by the community for the social movement. A key recommendation for future research is to incorporate the degree of on-going activities after a campaign into the model

Higher education, 1965–2005: Global convergence?

Several scholars describe and predict the global convergence of national higher-educational curricular organization while others claim that national mechanisms are working to counter such a trend. This study is an attempt to expand the empirical scope of the existing inquires concerning the convergence thesis. The data cover 71 countries and 75% of the world population over the period 1965–2005. The central hypothesis is that countries diverged during the period in terms of higher-educational curricular organization. Indeed, several specific hypotheses are developed and tested regarding national factors that might lead to such divergent movement: as countries experience rising business power, they will allocate more resources to science and applied divisions; as countries undergo democratization, they will give more resources to humanities and basic branches; as countries become more economically advanced, they will set aside more resources to humanities and basic divisions; and, as countries go through industrialization, they will allocate more resources to applied natural science, while doing the opposite at the post-industrialization stage. For evaluating these hypotheses, statistical measures of dispersion and random-effect ANCOVA with heterogeneous level-1 variance are used. The empirical results of this examination do not support the central hypothesis. Instead, it is found that the diversity of national curricular organization became smaller during 1965–2005 on the global scale and national factors might have led to such convergence. Overall, theoretical predictions seem to match empirical outcomes for subordinate hypotheses on national business power, democratization, economic advance relative to high-income countries and industrial development. Especially, it is discovered that countries midst intensive industrialization drives, particularly the late developers who are trying to catch up, put more focus on applied natural science in higher education than do other countries. And only those whose GDP per capita are as high as the average of organic members of the core zone might reduce their emphasis on applied natural science as their economies advance relative to other high-income countries. As international competition intensifies, even the majority of high-income countries might take the option of jumping on the bandwagon by emphasizing more on applied natural science in higher education

Living wage campaigns and community-based labor movements in the twenty-first century

This thesis explores the causes and consequences of living wage campaigns in the United States. Continuing shifts in U.S. ethnic politics engender a variety of opportunities for these campaigns to ultimately enhance the welfare of the working class and minorities. Researchers have concluded that living wage campaigns acquire all basic components of social movements, but have not developed a model for explaining variation in the causes and consequences of living wage campaigns. We present a model in which the following are key elements: the strength of both the social movement coalition, the government/business opposition, the mode of coalition-opposition interaction, media portrayal, community support, and the degree of policy and institutional transformation. Analysis of case studies of the living wage campaigns in Baltimore, Chicago, Los Angeles, and Miami between 1994 and 1999 is based on information obtained in articles in Labor Notes, Labor Studies Journal, and New Labor Forum. Coalition-opposition interaction was more likely to be characterized by dialogue than conflict when the coalition before the campaign and the opposition before the campaign were both strong or both weak. The degree of policy and institution transformation was greater when there was more support by the community for the social movement. A key recommendation for future research is to incorporate the degree of on-going activities after a campaign into the model.</p

Application of Artificial Intelligence in Early Diagnosis of Spontaneous Preterm Labor and Birth

This study reviews the current status and future prospective of knowledge on the use of artificial intelligence for the prediction of spontaneous preterm labor and birth (&ldquo;preterm birth&rdquo; hereafter). The summary of review suggests that different machine learning approaches would be optimal for different types of data regarding the prediction of preterm birth: the artificial neural network, logistic regression and/or the random forest for numeric data; the support vector machine for electrohysterogram data; the recurrent neural network for text data; and the convolutional neural network for image data. The ranges of performance measures were 0.79&ndash;0.94 for accuracy, 0.22&ndash;0.97 for sensitivity, 0.86&ndash;1.00 for specificity, and 0.54&ndash;0.83 for the area under the receiver operating characteristic curve. The following maternal variables were reported to be major determinants of preterm birth: delivery and pregestational body mass index, age, parity, predelivery systolic and diastolic blood pressure, twins, below high school graduation, infant sex, prior preterm birth, progesterone medication history, upper gastrointestinal tract symptom, gastroesophageal reflux disease, Helicobacter pylori, urban region, calcium channel blocker medication history, gestational diabetes mellitus, prior cone biopsy, cervical length, myomas and adenomyosis, insurance, marriage, religion, systemic lupus erythematosus, hydroxychloroquine sulfate, and increased cerebrospinal fluid and reduced cortical folding due to impaired brain growth

Explainable Artificial Intelligence in the Early Diagnosis of Gastrointestinal Disease

This study reviews the recent progress of explainable artificial intelligence for the early diagnosis of gastrointestinal disease (GID). The source of data was eight original studies in PubMed. The search terms were “gastrointestinal” (title) together with “random forest” or ”explainable artificial intelligence” (abstract). The eligibility criteria were the dependent variable of GID or a strongly associated disease, the intervention(s) of artificial intelligence, the outcome(s) of accuracy and/or the area under the receiver operating characteristic curve (AUC), the outcome(s) of variable importance and/or the Shapley additive explanations (SHAP), a publication year of 2020 or later, and the publication language of English. The ranges of performance measures were reported to be 0.70–0.98 for accuracy, 0.04–0.25 for sensitivity, and 0.54–0.94 for the AUC. The following factors were discovered to be top-10 predictors of gastrointestinal bleeding in the intensive care unit: mean arterial pressure (max), bicarbonate (min), creatinine (max), PMN, heart rate (mean), Glasgow Coma Scale, age, respiratory rate (mean), prothrombin time (max) and aminotransferase aspartate (max). In a similar vein, the following variables were found to be top-10 predictors for the intake of almond, avocado, broccoli, walnut, whole-grain barley, and/or whole-grain oat: Roseburia undefined, Lachnospira spp., Oscillibacter undefined, Subdoligranulum spp., Streptococcus salivarius subsp. thermophiles, Parabacteroides distasonis, Roseburia spp., Anaerostipes spp., Lachnospiraceae ND3007 group undefined, and Ruminiclostridium spp. Explainable artificial intelligence provides an effective, non-invasive decision support system for the early diagnosis of GID

Social Determinants of Association among Diabetes Mellitus, Visual Impairment and Hearing Loss in a Middle-Aged or Old Population: Artificial-Neural-Network Analysis of the Korean Longitudinal Study of Aging (2014–2016)

Background: This study introduces a new framework based on an artificial neural network (ANN) for testing whether social determinants are major determinants of association among diabetes mellitus, visual impairment and hearing loss in a middle-aged or old population. Methods: The data came from the Korean Longitudinal Study of Aging (2014&#8211;2016), with 6120 participants aged 45 years or more. The association was divided into eight categories: one category for having no disease, three categories for having one, three categories for having two and one category for having three. Variable importance, the effect of a variable on model performance, was used to evaluate the hypothesis based on whether family support, socioeconomic status and social activity in Y2014 are among the top 10 determinants of the association in the year 2016 (Y2016). Results: Based on variable importance from the ANN, brothers/sisters cohabiting (0.0167), voluntary activity (0.0148), income (0.0125), family activity (0.0125), parents alive (0.0121), leisure activity (0.0095) and meeting with friends (0.0092) in Y2014 are the top-10 determinants of comorbidity in Y2016. Conclusion: The findings of this study support the hypothesis, highlighting the importance of social determinants for the effective management of the comorbidities of the three diseases

Association between early preterm birth and maternal exposure to fine particular matter (PM10): A nation-wide population-based cohort study using machine learning.

Although preterm birth (PTB), a birth before 34 weeks of gestation accounts for only less than 3% of total births, it is a critical cause of various perinatal morbidity and mortality. Several studies have been conducted on the association between maternal exposure to PM and PTB, but the results were inconsistent. Moreover, no study has analyzed the risk of PM on PTB among women with cardiovascular diseases, even though those were thought to be highly susceptible to PM considering the cardiovascular effect of PM. Therefore, we aimed to evaluate the effect of PM10 on early PTB according to the period of exposure, using machine learning with data from Korea National Health Insurance Service (KNHI) claims. Furthermore, we conducted subgroup analysis to compare the risk of PM on early PTB among pregnant women with cardiovascular diseases and those without. A total of 149,643 primiparous singleton women aged 25 to 40 years who delivered babies in 2017 were included. Random forest feature importance and SHAP (Shapley additive explanations) value were used to identify the effect of PM10 on early PTB in comparison with other well-known contributing factors of PTB. AUC and accuracy of PTB prediction model using random forest were 0.9988 and 0.9984, respectively. Maternal exposure to PM10 was one of the major predictors of early PTB. PM10 concentration of 5 to 7 months before delivery, the first and early second trimester of pregnancy, ranked high in feature importance. SHAP value showed that higher PM10 concentrations before 5 to 7 months before delivery were associated with an increased risk of early PTB. The probability of early PTB was increased by 7.73%, 10.58%, or 11.11% if a variable PM10 concentration of 5, 6, or 7 months before delivery was included to the prediction model. Furthermore, women with cardiovascular diseases were more susceptible to PM10 concentration in terms of risk for early PTB than those without cardiovascular diseases. Maternal exposure to PM10 has a strong association with early PTB. In addition, in the context of PTB, pregnant women with cardiovascular diseases are a high-risk group of PM10 and the first and early second trimester is a high-risk period of PM10