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Dog loves being a bus driver as he diligently drops off animal passengers at their stops.

This article assesses Russian strategic narratives towards its interventions in Georgia (2008) and Ukraine (2014–16) based on a new database of 50 statements posted on the websites of the Russian Mission to the United Nations and the President of Russia homepage. By looking more broadly at Russian strategic narratives aimed at persuading other global actors and publics abroad and at home, this article identifies how Russia attempted to develop a story that could win global acceptance. This analysis shows that contrary to traditional Russian emphasis on sovereign responsibility and non-intervention, Russia supported claims for self-determination by separatist groups in Georgia and Ukraine. Russia used deception and disinformation in its strategic narratives as it mis-characterized these conflicts using Responsibility to Protect (R2P) language, yet mostly justified its own interventions through references to other sources of international law. Russian strategic narratives focused on delegitimizing the perceived opponents, making the case for the appropriateness of its own actions, and projecting what it proposed as the proper solution to the conflicts. It largely avoided making any references to its own involvement in the Donbas at all. Additionally, Russia’s focus on the protection of co-ethnics and Russian-speakers is reminiscent of interventions in the pre-R2P era.

Illustrations and simple, rhyming text reveal the day-to-day lives of cow space explorers, from what they look like, to how they keep busy, to how they deal with a noisy baby.

\"3 Landscape collects and organizes data into three categories-basic high school data, such as school locale, test score comparison, and high school and neighborhood indicators-that offers insight into high schools and neighborhoods.4 Among these indicators are quintessential measures of socioeconomic status, including college attendance, household structure, median family income, housing stability, education levels, and crime rate.5 To provide admissions officials with \"more consistent background information so they can fairly consider every student, no matter where they live and learn,\" these six indicators are averaged and presented on a 1-100 scale for both a student's high school and neighborhood.6 A higher score represents a \"higher level of challenge related to educational opportunities and outcomes\"; a lower score indicates less of a challenge in attaining academic success.7 This Note addresses potential Fourteenth Amendment8 challenges to the new Landscape profile. Notably, almost all affirmative action cases since Bakke have involved race and therefore have triggered strict scrutiny, including the recently-decided Fisher v. University of Texas at Austin case.13 However, several legal scholars suggest class based affirmative action programs, which account for socioeconomic disadvantage, could instead receive rational basis review.14 After building on the necessary factual and legal background in Parts I and II, Part III turns to the threshold requirement in any Fourteenth Amendment Equal Protection analysis,15 and assesses whether the College Board, a private not-for-profit organization, functions as a state actor because of its virtual monopoly over the college admissions process. Part IV evaluates the constitutionality of Landscape under the Equal Protection Clause, and argues rational basis review is appropriate because Landscape uses indicators that depend heavily on socioeconomic classifications.17 Alternatively, if Landscape is framed in terms of geographic locale, courts would still apply rational basis review.18 Next, this Note contends that the College Board has a legitimate interest in providing schools with consistent data reflective of students' socioeconomic upbringing because the Supreme Court of the United States previously held diversity in higher education is a compelling interest.19 Finally, despite criticism that Landscape is both underinclusive and overinclusive, this Note concludes that the program is rationally related to its proffered end.20 I. The College Board's Effort to Diversify Higher education The acronym \"SAT\" is notorious. Since 1926, countless college-bound students have taken the College Board's standardized test, the Scholastic Aptitude Test (\"SAT\").21 In the class of 2019, over 2.2 million students sat for the exam-a fourpercent increase from the class of 2018.22 This upward trend will persist, as the College Board now offers free weekday testing through the \"SAT School Day\" program.23 Simultaneously, the test-taking population will become more diverse because SAT School Day \"makes the SAT possible for students who . . . could not have tested on a weekend\" by \"eliminating barriers and simplifying the test-day experience. A.Landscape's Predecessor: The \"Environmental Context Dashboard\" In May 2019, the College Board announced a new initiative to measure socioeconomic adversity through the Environmental Context Dashboard (\"the Dashboard\").26 Originally, the Dashboard included information about a student's high school, comparative SAT scores, and contextual data on a student's neighborhood and school.27 The latter measure, dubbed an \"[A]dversity [S]core,\" was derived from thirty-one pieces of data pertaining to a student's neighborhood and high school, meant to gauge disadvantage.28 Admissions officials were to consider contextual data drawn from publicly available sources, such as the National Center for Education Statistics (\"NCES\") and the United States Census Bureau.29 Some of the pertinent factors were median family income, percentage of households in poverty, percentage of single parent households, percentage of adults without a high school or college degree, percentage of unemployed adults, and crime rate.30 Each of the thirty-one factors were displayed as a percentile between one and one-hundred, with a uniform distribution.31 A score of \"1\" indicated the least amount of disadvantage, while a score of \"100\" represented the most disadvantage in a particular category.32 The College Board then weighted the factors equally to generate a scaled score for both the student's high school and neighborhood.33 This \"Adversity Score\" was available as a percentile normed at the state and national levels.34 Again, scores were displayed on a scale of one to one-hundred, with a score of \"50\" representing an \"average\" level of socioeconomic disadvantage.35 Importantly, the Dashboard did not \"directly reflect an individual student;\" all data was aggregate and pertained to the census tract in which a student lived.36 Moreover, although the program's goal was to \"find students who have transcended their environments by examining factors that are correlated, according to research, with lower academic achievement and lower lifetime earnings,\" none of the contextual measures included race or ethnicity.37 The College Board elected for a holistic approach, hoping to provide a \"bigger package of data\" about applicants.38 In developing the Dashboard, the College Board drew from the research of economist Raj Chetty, who studied the impact of neighborhood lifetime earnings on scholastic achievement.39 The College Board also consulted with Richard D. Kahlenberg, a Senior Fellow at the Century Foundation and proponent of classbased affirmative action.40 Kahlenberg recommended a measure of socioeconomic disadvantage be included at the school and neighborhood levels.41 Although Kahlenberg also believed the Dashboard should have contained information about a student's family, he described the program as \"an enormous step forward.

Mice spend a very busy day at Cakes and More baking cakes, pies, tarts, rolls, and doughnut holes for their animal customers.

Tracking has been criticized for relegating disadvantaged students to lower track courses in which students encounter a greater lack of instructional support. While an end to tracks through detracking is a possible solution, there are concerns that detracking will create more heterogeneous classrooms, making it harder for teachers to provide adequate support to their students. Using the 2015 PISA dataset, this study conducts a causal inferential analysis to understand the differences in student perceptions of teaching in tracked and untracked environments. The results provide evidence that students’ needs, with respect to adaptation of instruction and provision of individualized feedback and support, are being met to the same extent on average in tracked and untracked science classes, suggesting that teachers may not necessarily have a harder time meeting the needs of students in untracked classes.

Determining body composition via mobile application may circumvent limitations of conventional methods. However, the accuracy of many technologies remains unknown. This investigation assessed the convergent and concurrent validity of a mobile application (LS) that employs 2-dimensional digital photography (LS2D) and 3-dimensional photonic scanning (LS3D). Measures of body composition including circumferences, waist-to-hip ratio (WHR), and body fat percentage (BF%) were obtained from 240 healthy adults using LS and a diverse set of conventional methods—Gulick tape, bioelectrical impedance analysis (BIA), and skinfolds. Convergent validity was consistently high—indicating these methods vary proportionally and can thus reliably detect changes despite individual measurement differences. The span of the Limits of Agreement (LoA) using LS were comparable to the LoA between conventional methods. LS3D exhibited high agreement relative to Gulick tape in the measurement of WHR, despite poor agreement with individual waist and hip circumferences. In BF%, LS2D exhibited high agreement with BIA and skinfold methods, whereas LS3D demonstrated low agreement. Interestingly, the low inferred bias between LS3D and DXA using existing data suggests that LS3D may have high agreement with dual-energy x-ray absorptiometry. Overall, the suitability of LS2D and LS3D to replace conventional methods must be based on an individual user’s criteria.