The hidden sexual minorities: machine learning approaches to estimate the sexual minority orientation among Beijing college students

Based on the fourth-wave Beijing College Students Panel Survey (BCSPS), this study aims to provide accurate estimation of the percentage of the potential sexual minorities among the Beijing college students by using machine learning methods. Specifically, we employ random forest (RF), an ensemble learning approach for classification and regression, to predict the sexual orientation of those who were not willing to disclose his/her inherent sexual identity. To overcome the imbalance problem arising from far different numerical proportion of sexual minority and majority members, we adopt the repeated random sub-sampling for training set by partitioning those who expressed heterosexual orientation into different number of splits and further combining each split with those who expressed sexual minority orientation. The prediction from 24-split random forest suggests that youths in Beijing with sexual minority orientation amount to 5.71%, almost two times that of the original estimation 3.03%. The results are robust to alternative learning methods and covariate sets. Besides, it is also suggested that random forest outperforms other learning algorithms, including AdaBoost, Naive Bayes, support vector machine (SVM), and logistic regression, in dealing with missing data, by showing higher accuracy, F1 score, and area under curve (AUC) value

Family status and women's career mobility during urban China's economic transition

Background: In contrast to the historical experience of Western welfare states, where social and family policies help create more integrated public-private spheres, marketization in China has presented a case of sphere separation. This phenomenon has important implications for the dynamics of gender inequality in economic transition. Objective: This article examines how family status is associated with women's career mobility in reform-era urban China and the impact of family on women's career choices across different reform stages. Methods: Based on retrospective data from the Chinese General Social Survey (CGSS) in 2008, we adopt discrete-time logit models to examine the effects of marriage and childbearing on women's upward mobility, the risk of labor market exit, and how the effects vary over time. Results: Chinese women in the workforce are adversely affected by marriage and having dependent children. They are more likely than men to experience (involuntary, in particular) job exit to fulfill their roles as wives and mothers and less likely to move up in the career ladder. This pattern is more prominent as the economic reform proceeds. Conclusions: Marketization has adversely affected Chinese women's career outcomes by increasing work-family tension after the work unit (danwei) system and socialist programs that supported working women were scrapped. Contribution: This study is one of the few empirical studies to attempt to explain the widening gender gap in China's job market from the perspective of family using the two-sphere separation framework. The framework originated in Western family studies but has been adapted to suit the context of urban China

Social prediction: a new research paradigm based on machine learning

Sociology is a science concerned with both the interpretive understanding of social action and the corresponding causal explanation, process, and result. A causal explanation should be the foundation of prediction. For many years, due to data and computing power constraints, quantitative research in social science has primarily focused on statistical tests to analyze correlations and causality, leaving predictions largely ignored. By sorting out the historical context of "social prediction," this article redefines this concept by introducing why and how machine learning can help prediction in a scientific way. Furthermore, this article summarizes the academic value and governance value of social prediction and suggests that it is a potential breakthrough in the contemporary social research paradigm. We believe that through machine learning, we can witness the advent of an era of a paradigm shift from correlation and causality to social prediction. This shift will provide a rare opportunity for sociology in China to become the international frontier of computational social sciences and accelerate the construction of philosophy and social science with Chinese characteristics

Literary destination familiarity and inbound tourism: evidence from mainland China

Destination familiarity is an important non-economic determinant of tourists’ destination choice that has not been adequately studied. This study posits a literary dimension to the concept of destination familiarity —that is, the extent to which tourists have gained familiarity with a given destination through literature—and seeks to investigate the impact of this form of familiarity on inbound tourism to Mainland China. Employing the English fiction dataset of the Google Books corpus, the New York Times annotated corpus, and the Time magazine corpus, we construct two types of destination familiarity based on literary texts: affection-based destination familiarity and knowledge-based destination familiarity. The results from dynamic panel estimation (1994–2004) demonstrate that the higher the degree of affection-based destination familiarity with a province in the previous year, the larger the number of inbound tourists the following year. Examining the influence of literature and its consumption on tourism activities sheds light on the dynamics of sustainable tourism development in emerging markets

The Association Between PM2.5 and Depression in China.

While China has been experiencing unprecedented economic growth, depression is becoming one of the most striking social and mental health problems in recent years. Such a paradox to progress may partially be due to the notoriously poor air quality of the country. To verify this argument, we constructed an index of the prevalence of depression (IPD) using internet search query volumes in Baidu to proxy the potential depression and examined how IPD is associated with PM2.5, the major air pollutant in China. Our results from 2-way fixed effects models reveal that a 100 μg·m-3 increase in previous week's PM2.5 in a city is significantly associated with 0.279 increase in its IPD, comparable to 7.34 hours decrease in weekly daylight, and such relationship is particularly pronounced in the spring and summer and in East and South areas. Our findings of large-scale pattern suggest that PM2.5 at current levels in China poses serious mental health risks

The association between PM2.5 exposure and suicidal ideation: a prefectural panel study.

BACKGROUND: Suicidal ideation is subject to serious underestimation among existing public health studies. While numerous factors have been recognized in affecting suicidal thoughts and behaviors (STB), the associated environmental risks have been poorly understood. Foremost among the various environment risks were air pollution, in particular, the PM2.5. The present study attempted to examine the relationship between PM2.5 level and local weekly index of suicidal ideation (ISI). METHODS: Using Internet search query volumes in Baidu (2017), the largest internet search engine in China, we constructed a prefectural panel data (278 prefectures, 52 weeks) and employed dynamic panel GMM system estimation to analyze the relationship between weekly concentration of PM2.5 (Mean = 87 μg·m- 3) and the index of suicidal ideation (Mean = 49.9). RESULTS: The results indicate that in the spring and winter, a 10 μg·m- 3 increase in the prior week's PM2.5 in a Chinese city is significantly associated with 0.020 increase in ISI in spring and a 0.007 increase in ISI in winter, after taking account other co-pollutants and meteorological conditions. CONCLUSION: We innovatively proposed the measure of suicidal ideation and provided suggestive evidence of a positive association between suicidal ideation and PM2.5 level

Antenna arrangement and energy-transfer pathways of PSI-LHCI from the moss Physcomitrella patens

Plants harvest light energy utilized for photosynthesis by light-harvesting complex I and II (LHCI and LHCII) surrounding photosystem I and II (PSI and PSII), respectively. During the evolution of green plants, moss is at an evolutionarily intermediate position from aquatic photosynthetic organisms to land plants, being the first photosynthetic organisms that landed. Here, we report the structure of the PSI-LHCI supercomplex from the moss Physcomitrella patens (Pp) at 3.23 angstrom resolution solved by cryo-electron microscopy. Our structure revealed that four Lhca subunits are associated with the PSI core in an order of Lhca1-Lhca5-Lhca2-Lhca3. This number is much decreased from 8 to 10, the number of subunits in most green algal PSI-LHCI, but the same as those of land plants. Although Pp PSI-LHCI has a similar structure as PSI-LHCI of land plants, it has Lhca5, instead of Lhca4, in the second position of Lhca, and several differences were found in the arrangement of chlorophylls among green algal, moss, and land plant PSI-LHCI. One chlorophyll, PsaF-Chl 305, which is found in the moss PSI-LHCI, is located at the gap region between the two middle Lhca subunits and the PSI core, and therefore may make the excitation energy transfer from LHCI to the core more efficient than that of land plants. On the other hand, energy-transfer paths at the two side Lhca subunits are relatively conserved. These results provide a structural basis for unravelling the mechanisms of light-energy harvesting and transfer in the moss PSI-LHCI, as well as important clues on the changes of PSI-LHCI after landing

Crystallinity Effects in Sequentially Processed and Blend-Cast Bulk-Heterojunction Polymer/Fullerene Photovoltaics

Although most polymer/fullerene-based solar cells are cast from a blend of the components in solution, it is also possible to sequentially process the polymer and fullerene layers from quasi-orthogonal solvents. Sequential processing (SqP) not only produces photovoltaic devices with efficiencies comparable to the more traditional bulk heterojunction (BHJ) solar cells produced by blend casting (BC) but also offers the advantage that the polymer and fullerene layers can be optimized separately. In this paper, we explore the morphology produced when sequentially processing polymer/fullerene solar cells and compare it to the BC morphology. We find that increasing polymer regioregularity leads to the opposite effect in SqP and BC BHJ solar cells. We start by constructing a series of SqP and BC solar cells using different types of poly(3-hexylthiophene) (P3HT) that vary in regioregulary and polydispersity combined with [6,6]-phenyl-C61-butyric-acid-methyl-ester (PCBM). We use grazing incidence wide-angle X-ray scattering to demonstrate how strongly changes in the P3HT and PCBM crystallinity upon thermal annealing of SqP and BC BHJ films depend on polymer regioregularity. For SqP devices, low regioregularity P3HT films that possess more amorphous regions allow for more PCBM crystallite growth and thus show better photovoltaic device efficiency. On the other hand, highly regioregular P3HT leads to a more favorable morphology and better device efficiency for BC BHJ films. Comparing the photovoltaic performance and structural characterization indicates that the mechanisms controlling morphology in the active layers are fundamentally different for BHJs formed via SqP and BC. Most importantly, we find that nanoscale morphology in both SqP and BC BHJs can be systematically controlled by tuning the amorphous fraction of polymer in the active layer. © 2014 American Chemical Society