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Parker is a thief who has an unusual code. He doesn't steal from the poor or hurt innocent people. He is asked to join four other guys, one of whom is related to a known mobster. They pull off the job flawlessly and Parker wants to part ways with them. When he refuses to join them for another job, they try to kill him. They dispose of his body, but someone finds him ... still alive. After recovering, he sets out to get back at the ones who tried to kill him, another one of his codes.

How is it that recipients of white privilege deny the role they play in reproducing racial inequality? Racing for Innocence addresses this question by examining the backlash against affirmative action in the late 1980s and early 1990s—just as courts, universities, and other institutions began to end affirmative action programs. This book recounts the stories of elite legal professionals at a large corporation with a federally mandated affirmative action program, as well as the cultural narratives about race, gender, and power in the news media and Hollywood films. Though most white men denied accountability for any racism in the workplace, they recounted ways in which they resisted—whether wittingly or not— incorporating people of color or white women into their workplace lives. Drawing on three different approaches—ethnography, narrative analysis, and fiction—to conceptualize the complexities and ambiguities of race and gender in contemporary America, this book makes an innovative pedagogical tool.

[Display omitted] The current study sought to determine if the release of COVID-19 vaccines influenced Alabama mothers’ attitudes and behaviors regarding HPV vaccination of their adolescent children. A social media survey was conducted August − September 2022 among mothers of adolescents ages 9–18, who self-identified as Alabama residents and their child(ren)’s primary medical decision maker. The survey assessed demographics, vaccine knowledge and opinions, vaccination history, influences on vaccination decisions, and how COVID-19 vaccine release affected vaccine opinions. Univariable and multivariable analyses were conducted. Of 241 responses, most participants were white (82.0 %, n = 196), non-Hispanic (96.6 %, n = 230), and privately insured (64.5 %, n = 151), with annual household incomes ≥$61,000 (45.4 %, n = 103). The majority (60.8 %) reported that their child either had or planned to receive the HPV vaccine. The release of COVID-19 vaccines did not change the majority of parental opinions towards HPV, with 78.5 % (n = 161) reporting no change. Among those who experienced a change, 25 % (n = 5) reported an increased likelihood of having their child vaccinated for HPV and 75 % (n = 15) reported a decrease in likelihood. Moderate and high HPV knowledge scores were associated in multivariable analysis with increased likelihood of having their child vaccinated for HPV (“moderate” knowledge AOR: 12.4, 95 % CI: 1.98–78.1; “high” knowledge AOR: 12.8, 95 % CI: 2.00–82.1). Positive HPV opinion scores in the univariable analysis similarly showed increased odds (AOR = 1.5). These findings indicate that, in this population, COVID-19 vaccine release did not significantly impact subsequent HPV vaccination decision making. Parental perceptions regarding vaccination are critical to informing future interventions.

In a union campaign that began in 1970 and ended in 1974, the University of California at Berkeley’s American Federation of State, County, and Municipal Employees (AFSCME) Local 1695’s secretaries published a clerical workers’ manifesto, participated in writing a formal affirmative action report with the librarian’s union, and filed a mass grievance against sex discrimination signed by three hundred clerical workers. Significantly, they rallied against sex discrimination with the slogan “equal pay for equivalent work.” Their campaign not only preceded the first comparable-worth campaign in 1978 in San Jose, California, but was linked directly to it through Local 1695 activists and their activism. This article complicates the origin story for the late-twentieth-century comparable-worth movement and highlights Local 1695’s partnership with librarians in crafting what historian Katherine Turk has called an “expanded interpretation of sex equality law.”

Hydraulic geometry parameters describing river hydrogeomorphic relationships are critical for determining a channel's capacity to convey water and sediment which is important for flood forecasting. Although well‐established, power‐law hydraulic geometry curves have been widely used to understand riverine systems and mapping flooding inundation worldwide for the past 70 years, we have become increasingly aware of their limitations. In the present study, we have moved beyond these traditional power‐law relationships, testing the ability of machine‐learning models to provide improved predictions of river width and depth. For this work, we have used an unprecedentedly large river measurement data set (HYDRoSWOT) as well as a suite of watershed predictor data to develop novel data‐driven approaches to better estimate river geometries over the contiguous United States (CONUS). Our Random Forest, XGBoost, and neural network models out‐performed the traditional, regionalized power law‐based hydraulic geometry equations for both width and depth, providing R‐squared values of as high as 0.75 for width and as high as 0.67 for depth, compared with R‐squared values of 0.45 for width and 0.18 for depth from the regional hydraulic geometry equations. Our results also show diverse performance outcomes across stream orders and geographical regions for the different machine‐learning models, demonstrating the value of using multi‐model approaches to maximize the predictability of river geometry. The developed models have been used to create the newly publicly available STREAM‐geo data set, which provides river width, depth, width/depth ratio, and river and stream surface area (%RSSA) for nearly 2.7 million NHDPlus stream reaches across the contiguous US. Plain Language Summary Scientists and river managers use measurements of river geometry such as width and depth to forecast floods and understand river behavior. However, the methods used to estimate river geometry that have been used for decades are imprecise and thus lead to poor predictions of river discharge dynamics. Here, we've used new machine learning‐based modeling approaches to provide better predictions of river width and depth. We tested different machine‐learning models, which were developed based on the HYDRoSWOT set of measurements of rivers across the U.S. These new models all provide better estimates of river width and depth than the old methods. Our research can help us to provide better estimates of flood dynamics and improve our understanding of rivers across the U.S. Key Points Machine Learning models outperform regional (physiographic) hydraulic geometry equations for predicting stream width and depth Model performance varies by stream orders and geographical regions, demonstrating the utility of multi‐model machine‐learning approaches The STREAM‐geo data set provides predictions of river width, depth, width‐to‐depth ratio, and river area for the NHDPlus stream reaches

Maps that provide pragmatic geographic and location information can also be used to document and describe fictional places. Readers have used the affordances of Google Maps to add settings from favorite books to this online information resource, demonstrating a complex form of reader response that I call readerly cartography. This practice aligns with an interdisciplinary scholarship on maps as culturally constructed texts. The effect of readerly cartography is to document and collocate communities of actual readers. KEYWORDS: literary mapping, reader response, reading culture, digital culture, digital humanities

Urban biodiversity offers important benefits to residents and may be crucial to reaching global biodiversity conservation targets, but little research has been conducted on how cities actually plan for biodiversity. In this study, we conducted a mixed methods content analysis of biodiversity plans by 39 cities around the world to determine whether they measured their actions, how they did so (via quantitative indicators and qualitative outputs), and what topics these actions and measures covered. We based our analytical framework on the Singapore Index on Cities' Biodiversity (also known as the City Biodiversity Index), a widely applied 23-indicator index that helps cities track their progress in biodiversity planning. The Singapore Index groups its indicators into the following three core components: native biodiversity, ecosystem services, and governance and management. For actions and measures not classifiable by the Singapore Index, we inductively derived additional categories. Across all plans, we identified 2,231 actions, 346 indicators, and 444 outputs. We found that all of the plans included actions, while 82% included measures (67% included indicators and 72% included outputs). Only 29% of actions were associated with a measure. Overall, the plans covered all of the categories in the Singapore Index, particularly within the core components of native biodiversity and governance and management, though some plans had a narrower focus. The 20 additional urban biodiversity topics that were not covered by the Singapore Index framework included socioeconomic considerations, data collection, genetic diversity, urban agriculture and forestry, green infrastructure, human-wildlife conflicts, indigenous concerns, and citizen science. Indicators were the most common measures for native biodiversity and ecosystem service topics, while outputs were the most common measures for governance and management. Our results may inform the revision and development of urban biodiversity indicators in the post-2020 framework and of other initiatives that guide cities in contributing to local and global biodiversity goals.

New York State has been an epicenter for both the US coronavirus disease 2019 (COVID-19) and HIV/AIDS epidemics. Persons living with diagnosed HIV may be more prone to COVID-19 infection and severe outcomes, yet few studies have assessed this possibility at a population level. To evaluate the association between HIV diagnosis and COVID-19 diagnosis, hospitalization, and in-hospital death in New York State. This cohort study, conducted in New York State, including New York City, between March 1 and June 15, 2020, matched data from HIV surveillance, COVID-19 laboratory-confirmed diagnoses, and hospitalization databases to provide a full population-level comparison of COVID-19 outcomes between persons living with diagnosed HIV and persons living without diagnosed HIV. Diagnosis of HIV infection through December 31, 2019. The main outcomes were COVID-19 diagnosis, hospitalization, and in-hospital death. COVID-19 diagnoses, hospitalizations, and in-hospital death rates comparing persons living with diagnosed HIV with persons living without dianosed HIV were computed, with unadjusted rate ratios and indirect standardized rate ratios (sRR), adjusting for sex, age, and region. Adjusted rate ratios (aRRs) for outcomes specific to persons living with diagnosed HIV were assessed by age, sex, region, race/ethnicity, transmission risk, and CD4+ T-cell count-defined HIV disease stage, using Poisson regression models. A total of 2988 persons living with diagnosed HIV (2109 men [70.6%]; 2409 living in New York City [80.6%]; mean [SD] age, 54.0 [13.3] years) received a diagnosis of COVID-19. Of these persons living with diagnosed HIV, 896 were hospitalized and 207 died in the hospital through June 15, 2020. After standardization, persons living with diagnosed HIV and persons living without diagnosed HIV had similar diagnosis rates (sRR, 0.94 [95% CI, 0.91-0.97]), but persons living with diagnosed HIV were hospitalized more than persons living without diagnosed HIV, per population (sRR, 1.38 [95% CI, 1.29-1.47]) and among those diagnosed (sRR, 1.47 [95% CI, 1.37-1.56]). Elevated mortality among persons living with diagnosed HIV was observed per population (sRR, 1.23 [95% CI, 1.07-1.40]) and among those diagnosed (sRR, 1.30 [95% CI, 1.13-1.48]) but not among those hospitalized (sRR, 0.96 [95% CI, 0.83-1.09]). Among persons living with diagnosed HIV, non-Hispanic Black individuals (aRR, 1.59 [95% CI, 1.40-1.81]) and Hispanic individuals (aRR, 2.08 [95% CI, 1.83-2.37]) were more likely to receive a diagnosis of COVID-19 than White individuals, but they were not more likely to be hospitalized once they received a diagnosis or to die once hospitalized. Hospitalization risk increased with disease progression to HIV stage 2 (aRR, 1.29 [95% CI, 1.11-1.49]) and stage 3 (aRR, 1.69 [95% CI, 1.38-2.07]) relative to stage 1. In this cohort study, persons living with diagnosed HIV experienced poorer COVID-related outcomes relative to persons living without diagnosed HIV; Previous HIV diagnosis was associated with higher rates of severe disease requiring hospitalization, and hospitalization risk increased with progression of HIV disease stage.

In April 1968, two Berkeley campus unions—the American Federation of State, County, and Municipal Employees (AFSCME) Local 1695 representing clerical, technical, and professional workers, and the American Federation of Teachers (AFT) Local 1570 representing graduate students—held a work-stoppage and a teach-in on “campus racism” to honor the memory of the Reverend Martin Luther King Jr. who had been tragically assassinated in Memphis. Inspired by King's work and the AFSCME sanitation workers strike that he supported, the teach-in became a series of workshops that ultimately led to the development of a “white paper” with statistical data highlighting the ways the university harbored racism in its employment practices and in its admission of undergraduate and graduate students. Among its many demands, it called for the University: “to hire black, brown and red workers until the ratio of employees from these groups equals the ratio in the population; bring minority student enrollment and employment up to population ratios . . . publish the University census report showing the percentage of black, brown, and red employees by department; and make an additional report showing the classifications and promotions of black, brown, and red people in each department.”

Background An inability to successfully recruit participants into clinical research has consequences that negatively affect the conduct and reliability of research studies. Understanding facilitators of research participation, namely motives for participation and preferred research outcomes, may improve recruitment and retention of clinical trials related to chronic pain. The present study explored research participation facilitators among individuals with chronic pain and their association with demographic characteristics, pain-related characteristics, and factors related to future research engagement. Methods Individuals from Michigan who were 18 years or older and self-reported having chronic pain completed an online survey assessing motives for research participation and desired research outcomes. Analyses were conducted in three stages. First, we evaluated underlying factors of motives for participation and research outcome preferences using principal components analysis. Second, we classified individuals according to their patterns of facilitators using latent profile analysis. Finally, we evaluated differences between facilitator profiles in demographic characteristics, pain-related characteristics, and factors related to future research engagement using χ 2 analyses and Kruskal-Wallis rank sum tests. Results Three components of motives for research participation were identified: social engagement/enjoyment; pain improvement/advancing science; and compensation. Three components of research outcome preferences were identified: co-occurring symptom reduction; behavior reduction modification; and pain and function improvement. Four potential patient-centered profiles utilizing these dimensions of facilitators were identified that had unique demographic characteristics, research participation willingness, and treatment interest. Conclusions Our data provide a framework of motives and research outcome preferences that may inform recruitment and retention in chronic pain research. It also gives an indication of who may respond best to active or passive recruitment strategies that appeal to a given motive or preferred outcome. This information may be useful for improving recruitment and to monitor any potential biases in participant samples.