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The pastures in China’s pastoral areas have a \"small and scattered\" distribution, which results in overloading and overgrazing, ecological degradation, and other problems. These problems have constrained the sustainable development of grassland animal husbandry. Governments at all levels have implemented measures to promote the transfer of pastureland for herders, which has become a meaningful way to optimize the allocation of pastureland resources and improve the ecological environment in the second instance. In order to deeply explore the influence of pasture turn-out on herders’ traditional lifestyle and to promote the rational utilization of pastureland in pastoral areas, the study is based on 437 interview data of herders in Inner Mongolia and Xinjiang. It adopts the Binary Logit model to analyze the influence and mechanism of herders’ willingness to turn out of pastureland in terms of ethnic sentiment and social differentiation. The results show that (1) Nomadic and mutual aid sentiments significantly and negatively affect herders’ willingness to transfer pasture. The stronger the national sentiment, the lower the willingness to transfer pasture and the more cautious the behaviour of transferring pasture. (2) The proportion of pasture income and the proportion of pasture labour significantly and negatively affect the herders’ willingness to transfer pasture. Specifically, the increase in herders’s family pasture income and the proportion of pasture labour will reduce the willingness to transfer pasture. The conclusion still holds after further robustness checks by introducing instrumental variables, changing the regression model, and replacing the sample size. (3) At the macro level, the government needs to take advantage of the situation and tap the positive role of national sentiment in rural revitalization; at the micro level of herders, it needs to enhance their employability, enrich income channels, stimulate the endogenous dynamics of social differentiation in the development of herders’ livelihoods, and realize the effective matching of pasture resources.

In this article I discuss the way affect has featured in discussions of identity, focusing on ethnic and national identities. While affect features in most discussions of ethnicity it has mostly been dismissed as a testament to the irrationality and dangerous qualities of the identity in question. Such discussions adopt a simplistic model of human psychology, usually based on a hydraulic model of the emotions. After considering some recent and pioneering work that foregrounds the role of affectivity in group formations, I proceed to outline the basis for a phenomenological psychology of affect and group identities incorporating insights from psychoanalysis and phenomenology. One cannot begin to discuss the proper role of identity in the public sphere without first considering the emotional dynamics that underlie such group formations.

This chapter contains sections titled: Introduction Post‐Colonial State Building in Guinea Forécariah During the 2007 Strikes The Perpetual Construction of State Conclusion References

This chapter contains sections titled: Introduction The Angolan State: Centralization and Regional Inequality Negotiating the Local State: The Sub‐National Reforms Regional Elite Associations in Huíla Province Representation: The Two Regional Elite Associations Compared Conclusion References

The Color of Successtells of the astonishing transformation of Asians in the United States from the \"yellow peril\" to \"model minorities\"--peoples distinct from the white majority but lauded as well-assimilated, upwardly mobile, and exemplars of traditional family values--in the middle decades of the twentieth century. As Ellen Wu shows, liberals argued for the acceptance of these immigrant communities into the national fold, charging that the failure of America to live in accordance with its democratic ideals endangered the country's aspirations to world leadership. Weaving together myriad perspectives, Wu provides an unprecedented view of racial reform and the contradictions of national belonging in the civil rights era. She highlights the contests for power and authority within Japanese and Chinese America alongside the designs of those external to these populations, including government officials, social scientists, journalists, and others. And she demonstrates that the invention of the model minority took place in multiple arenas, such as battles over zoot suiters leaving wartime internment camps, the juvenile delinquency panic of the 1950s, Hawaii statehood, and the African American freedom movement. Together, these illuminate the impact of foreign relations on the domestic racial order and how the nation accepted Asians as legitimate citizens while continuing to perceive them as indelible outsiders. By charting the emergence of the model minority stereotype,The Color of Successreveals that this far-reaching, politically charged process continues to have profound implications for how Americans understand race, opportunity, and nationhood.

The rapid integration of Artificial Intelligence (AI) into the healthcare field has occurred with little communication between computer scientists and doctors. The impact of AI on health outcomes and inequalities calls for health professionals and data scientists to make a collaborative effort to ensure historic health disparities are not encoded into the future. We present a study that evaluates bias in existing Natural Language Processing (NLP) models used in psychiatry and discuss how these biases may widen health inequalities. Our approach systematically evaluates each stage of model development to explore how biases arise from a clinical, data science and linguistic perspective. A literature review of the uses of NLP in mental health was carried out across multiple disciplinary databases with defined Mesh terms and keywords. Our primary analysis evaluated biases within 'GloVe' and 'Word2Vec' word embeddings. Euclidean distances were measured to assess relationships between psychiatric terms and demographic labels, and vector similarity functions were used to solve analogy questions relating to mental health. Our primary analysis of mental health terminology in GloVe and Word2Vec embeddings demonstrated significant biases with respect to religion, race, gender, nationality, sexuality and age. Our literature review returned 52 papers, of which none addressed all the areas of possible bias that we identify in model development. In addition, only one article existed on more than one research database, demonstrating the isolation of research within disciplinary silos and inhibiting cross-disciplinary collaboration or communication. Our findings are relevant to professionals who wish to minimize the health inequalities that may arise as a result of AI and data-driven algorithms. We offer primary research identifying biases within these technologies and provide recommendations for avoiding these harms in the future.

Background: Anecdotal reports suggest a rise in anti-Asian racial attitudes and discrimination in response to COVID-19. Racism can have significant social, economic, and health impacts, but there has been little systematic investigation of increases in anti-Asian prejudice. Methods: We utilized Twitter’s Streaming Application Programming Interface (API) to collect 3,377,295 U.S. race-related tweets from November 2019–June 2020. Sentiment analysis was performed using support vector machine (SVM), a supervised machine learning model. Accuracy for identifying negative sentiments, comparing the machine learning model to manually labeled tweets was 91%. We investigated changes in racial sentiment before and following the emergence of COVID-19. Results: The proportion of negative tweets referencing Asians increased by 68.4% (from 9.79% in November to 16.49% in March). In contrast, the proportion of negative tweets referencing other racial/ethnic minorities (Blacks and Latinx) remained relatively stable during this time period, declining less than 1% for tweets referencing Blacks and increasing by 2% for tweets referencing Latinx. Common themes that emerged during the content analysis of a random subsample of 3300 tweets included: racism and blame (20%), anti-racism (20%), and daily life impact (27%). Conclusion: Social media data can be used to provide timely information to investigate shifts in area-level racial sentiment.

Large racial and ethnic disparities in adverse birth outcomes persist. Increasing evidence points to the potential role of racism in creating and perpetuating these disparities. Valid measures of area-level racial attitudes and bias remain elusive, but capture an important and underexplored form of racism that may help explain these disparities. Cultural values and attitudes expressed through social media reflect and shape public norms and subsequent behaviors. Few studies have quantified attitudes toward different racial groups using social media with the aim of examining associations with birth outcomes. We used Twitter data to measure state-level racial sentiments and investigate associations with preterm birth (PTB) and low birth weight (LBW) in a multiracial or ethnic sample of mothers in the United States. A random 1% sample of publicly available tweets from January 1, 2011, to December 31, 2021, was collected using Twitter's Academic Application Programming Interface (N=56,400,097). Analyses were on English-language tweets from the United States that used one or more race-related keywords. We assessed the sentiment of each tweet using support vector machine, a supervised machine learning model. We used 5-fold cross-validation to assess model performance and achieved high accuracy for negative sentiment classification (91%) and a high F1 score (84%). For each year, the state-level racial sentiment was merged with birth data during that year (~3 million births per year). We estimated incidence ratios for LBW and PTB using log binomial regression models, among all mothers, Black mothers, racially minoritized mothers (Asian, Black, or Latina mothers), and White mothers. Models were controlled for individual-level maternal characteristics and state-level demographics. Mothers living in states in the highest tertile of negative racial sentiment for tweets referencing racial and ethnic minoritized groups had an 8% higher (95% CI 3%-13%) incidence of LBW and 5% higher (95% CI 0%-11%) incidence of PTB compared to mothers living in the lowest tertile. Negative racial sentiment referencing racially minoritized groups was associated with adverse birth outcomes in the total population, among minoritized mothers, and White mothers. Black mothers living in states in the highest tertile of negative Black sentiment had 6% (95% CI 1%-11%) and 7% (95% CI 2%-13%) higher incidence of LBW and PTB, respectively, compared to mothers living in the lowest tertile. Negative Latinx sentiment was associated with a 6% (95% CI 1%-11%) and 3% (95% CI 0%-6%) higher incidence of LBW and PTB among Latina mothers, respectively. Twitter-derived negative state-level racial sentiment toward racially minoritized groups was associated with a higher risk of adverse birth outcomes among the total population and racially minoritized groups. Policies and supports establishing an inclusive environment accepting of all races and cultures may decrease the overall risk of adverse birth outcomes and reduce racial birth outcome disparities.

The number of Chinese tourists visiting Malaysia has not returned to pre-pandemic levels, raising concerns that recent negative news stories widely circulated on Chinese social media may be a contributing factor. Due to the limited number of reviews, sentiment analysis struggles to determine whether negative news significantly affects tourists’ emotions and visitor numbers. This study employs empirical data to validate its impact, clarifying its actual influence on image discrepancy and tourist arrivals. Additionally, while managing image discrepancy is acknowledged as important in tourism, academic debate persists regarding whether negative news contributes to these discrepancies. This study conducted in-depth interviews with 30 participants, comprising 18 individual attendees and 12 attendees from 4 small seminars. Participants included recent visitors to Malaysia, prospective tourists who canceled their plans, and Malaysian tourism practitioners. Using the Context-Adaptive Grounded Theory (CAGT) methodology, the study explored whether sudden negative news events significantly influence Chinese tourists’ travel decisions and identified specific factors contributing to the discrepancy between Chinese tourists’ perceptions of Malaysia’s tourism image and the image projected by Destination Marketing Organizations (DMOs). The findings revealed that negative news was not the dominant factor driving image discrepancy. Instead, cultural differences—stemming from unclear understanding of religious and local cultures, as well as language barriers—were the primary causes. Additionally, inefficiencies in DMOs’ promotion of Malaysia’s multi-ethnic identity and an overemphasis on medical tourism, which failed to attract Chinese tourists, were identified as secondary factors. This study provides valuable insights into the complex impact of sudden events on tourism and offers guidance for Destination Management Organizations to better align projected and perceived destination images to facilitate tourism recovery.

Individuals with autism spectrum disorder (ASD) demonstrate impairments with pragmatic (social) language, including narrative skills and conversational abilities. We aimed to quantitatively characterize narrative performance in ASD using natural language processing techniques: sentiment and language abstraction analyses based on the Linguistic Category Model. Individuals with ASD and with typical development matched for age, gender, ethnicity, and verbal and nonverbal intelligence quotients produced language samples during two standardized tasks from the Autism Diagnostic Observation Schedule, Second Edition assessment: Telling a Story from a Book and Description of a Picture. Only the narratives produced during the Book Task differed between ASD and control groups in terms of emotional polarity and language abstraction. Participants with typical development used words with positive sentiment more often in comparison to individuals with ASD. In the case of words with negative sentiment, the differences were marginally significant (participants with typical development used words with negative sentiment more often). The Book Task narratives of individuals with ASD were also characterized by a lower level of language abstraction than narratives of peers with typical development. Linguistic abstraction was strongly positively correlated with a higher number of words with emotional polarity. Neither linguistic abstraction nor emotional polarity correlated with participants' age or verbal and nonverbal IQ. The results support the promise of sentiment and language abstraction analyses as a useful tool for the quantitative, fully automated assessment of narrative abilities among individuals with ASD.