Suchergebnisse

Christopher C. Langdell (1826-1906) is one of the most influential figures in the history of American professional education. As dean of Harvard Law School from 1870 to 1895, he conceived, designed, and built the educational model that leading professional schools in virtually all fields subsequently emulated. In this first full-length biography of the educator and jurist, Bruce Kimball explores Langdell's controversial role in modern professional education and in jurisprudence. Langdell founded his model on the idea of academic meritocracy. According to this principle, scholastic achievement determines or should determine one's merit in professional life. Cf. Introduction, page 2

The English and continental roots of American legal education --American antecedents of Harvard Law School --Founding a university professional school of law --The school saved --Joseph Story's law school in the young republic --The Greenleaf transition --The gathering storm --Civil war and aftermath --Dean Langdall, first casebooks, and Justice Holmes --Curricular and pedagogical revolution --Creating the "new system" of legal education --The paths of four students --The "new system," triumphant and invidious --Students of color at Harvard Law School --Beloved Dean Ames.

Among explanations for the escalating cost of higher education in the United States, two economic theories predominate: the revenue theory of cost and cost disease theory. Since its formulation in the 1960s, distinguished economists have concluded that cost disease theory has convincingly been proven to explain cost escalation in higher education. This article examines three historical dimensions of the cost disease in higher education from the 1870s to the 2010s. First, we explain how the scholarship on the cost disease in higher education has developed over the past 50 years. Second, we concurrently analyze the historical data and the reasoning presented by economists in support of the view that cost disease theory explains cost escalation in higher education. This analysis concludes that the scholarship over the last 50 years provides little validation for that explanation. Finally, we present historical research on cost trends in US higher education from 1875 to 1930. This formative period in US higher education witnessed enormous growth in the national economy. Due to the growth in productivity, cost disease theory would expect costs in higher education, a personal services industry, to rise sharply relative both to costs generally and to the national income. But this historical research reveals that the per capita cost of higher education grew very slowly over this period. These findings consist with our analysis of cost disease scholarship. We therefore conclude that there is little validation that cost disease theory explains cost escalation in US higher education from the 1870s to the 2010s, though it may explain some periods within that span.

We examined the repellency of a commercially available animal repellent to determine the efficacy of its application to objects that are attractive to coyotes (Canis latrans). Specifically, we aimed to both prevent chewing behavior by coyotes on a nylon-like strapping material, which is used to construct barrier-arresting systems on military airstrips, and determine the ability of the solution to prevent the animals from repeating the undesired behavior. We mixed Ropel® Animal and Rodent Repellent with a liquid latex sticker to form a 2% latex and 98% Ropel solution. We used a 2% latex and 98% water solution as a control. The solutions were applied to test material placed in coyote pens. We exposed 12 mated pairs of coyotes to the Ropel and control in a 2-choice test and recorded behavior toward the materials using camera traps. Photographs and the condition of the material were used to determine when, and if, individual coyotes approached, made contact with, tasted, repeatedly tasted, or destroyed the material. There was no difference between the number of treatment and control materials tasted, but significantly more control materials were repeatedly tasted than treatment materials. However, there was no difference between the number of treatment and control materials destroyed. While results suggest that there are some repellent properties in the Ropel solution after initial tasting, we do not recommend this product be relied upon as a coyote-specific repellent.

Outbreaks of avian influenza virus (AIV) infection included the spread of highly pathogenic AIV in commercial poultry and backyard flocks in the spring of 2015. This resulted in estimated losses of more than $8.5 million from federal government expenditures, $1.6 billion from direct losses to produces arising from destroyed turkey and chicken egg production, and economy-wide indirect costs of $3.3 billion from impacts on retailers and the food service industries. Additionally, these outbreaks resulted in the death or depopulation of nearly 50 million domestic birds. Domesticated male ferrets (Mustela putorius furo) were trained to display a specific conditioned behavior (i.e. active scratch alert) in response to feces from AIV-infected mallards in comparison to feces from healthy ducks. In order to establish that ferrets were identifying samples based on odors associated with infection, additional experiments controlled for potentially confounding effects, such as: individual duck identity, housing and feed, inoculation concentration, and day of sample collection (post-infection). A final experiment revealed that trained ferrets could detect AIV infection status even in the presence of samples from mallards inoculated with Newcastle disease virus or infectious laryngotracheitis virus. These results indicate that mammalian biodetectors are capable of discriminating the specific odors emitted from the feces of non-infected versus AIV infected mallards, suggesting that the health status of waterfowl can be evaluated non-invasively for AIV infection via monitoring of volatile fecal metabolites. Furthermore, in situ monitoring using trained biodetectors may be an effective tool for assessing population health.

A critical history of the Americanization of legal education in fourteen countriesThe second half of the twentieth century witnessed the export of American power—both hard and soft—throughout the world. What role did US cultural and economic imperialism play in legal education? American Legal Education Abroad offers an unprecedented and surprising picture of the history of legal education in fourteen countries beyond the United States.Each study in this book represents a critical history of the Americanization of legal education, reexamining prevailing narratives of exportation, transplantation, and imperialism. Collectively, these studies challenge the conventional wisdom that American ideas and practices have dominated globally. Editors Susan Bartie and David Sandomierski and their contributors suggest that to understand legal education and to respond thoughtfully to the mounting present-day challenges, it is essential to look beyond a particular region and consider not only the ideas behind legal education but also the broader historical, political, and cultural factors that have shaped them.American Legal Education Abroad begins with an important foundational history by leading Harvard Law School historian Bruce Kimball, who explains the factors that created a transportable American legal model, and the book concludes with reflections from two prominent American law professors, Susan Carle and Bob Gordon, whose observations on recent disruptions within US law schools suggest that their influence within the global order of legal education may soon fall into further decline. This book should be considered an invaluable resource for anyone in the field of law

Increasing the accuracy of crop productivity estimates is a key element in planning adaptation strategies to ensure global food security under climate change. Process-based crop models are effective means to project climate impact on crop yield, but have large uncertainty in yield simulations. Here, we show that variations in the mathematical functions currently used to simulate temperature responses of physiological processes in 29 wheat models account for >50% of uncertainty in simulated grain yields for mean growing season temperatures from 14 °C to 33 °C. We derived a set of new temperature response functions that when substituted in four wheat models reduced the error in grain yield simulations across seven global sites with different temperature regimes by 19% to 50% (42% average). We anticipate the improved temperature responses to be a key step to improve modelling of crops under rising temperature and climate change, leading to higher skill of crop yield projections. Process-based modelling of crop growth is an effective way of representing how crop genotype, environment and management interactions affect crop production to aid tactical and strategic decision making1. Process-based crop models are increasingly used to project the impact of climate change on crop yield2. However, current models produce different results, creating large uncertainty in crop yield simulations3. A model inter-comparison study within the Agricultural Model Inter-comparison and Improvement Project (AgMIP)4 of 29 widely used wheat models against field experimental data revealed that there is more uncertainty in simulating grain yields from the different models than from 16 different climate change scenarios3. The greatest uncertainty was in modelling crop responses to temperature3,5. Similar results were found with rice and maize crops6,7. Such uncertainty should be reduced before informing decision making in agriculture and government policy. Here, we show contrasting differences in temperature response functions of key physiological processes adopted in the 29 crop models. We reveal opportunities for improving simulation of temperature response in crop models to reduce the uncertainty in yield simulations. We aim to reassess the scientific assumptions underlying model algorithms and parameterization describing temperature-sensitive physiological processes, using wheat, one of the most important staple crops globally, as an example. We hypothesized that: (1) the difference among models in assumed temperature responses is the largest source of the uncertainty in simulated yields; and (2) the uncertainty in the multi-model ensemble results can be reduced by improving the science for modelling temperature response of physiological processes. Temperature affects crop performance primarily through its impact on (1) the rate of phenological development from seed germination to crop maturity, including the fulfilment of cold requirement (vernalisation); (2) the initiation and expansion of plant organs; (3) photosynthesis and respiration, considered either separately or combined as net biomass growth simulated using radiation use efficiency (RUE)8; and (4) the senescence, sterility or abortion of plant organs. All 29 models simulate these processes, except for sterility and abortion, in response to temperature change. Here, we compare the temperature functions of these four categories of physiological processes built into the 29 wheat models and identify the representative response types. We analyse how different temperature response functions affected simulations of wheat growth compared to observations in a field experiment8,9,10, in which well-fertilized and irrigated wheat grew under contrasting sowing dates and temperature environments (Hot Serial Cereal (HSC) experiment). We further evaluate the impact of the different response types by implementing them in two models (APSIM and SiriusQuality) and analysing their results against the HSC data and an additional global dataset from the International Heat Stress Genotpye Experiment (IHSGE)8 carried out by the International Maize and Wheat Improvement Center (CIMMYT). More importantly, we derive, based on newest knowledge and data, a set of new temperature response functions for the key physiological processes of wheat and demonstrate that when substituted in four wheat models the new functions reduced the error in grain yield simulations across seven global sites with different temperature regimes covered by the IHSGE data.