The phenomenal growth of global pharmaceutical sales and the quest for innovation are driving an unprecedented search for human test subjects, particularly in middle- and low-income countries. Our ...hope for medical progress increasingly depends on the willingness of the world's poor to participate in clinical drug trials. While these experiments often provide those in need with vital and previously unattainable medical resources, the outsourcing and offshoring of trials also create new problems. In this groundbreaking book, anthropologist Adriana Petryna takes us deep into the clinical trials industry as it brings together players separated by vast economic and cultural differences. Moving between corporate and scientific offices in the United States and research and public health sites in Poland and Brazil,When Experiments Traveldocuments the complex ways that commercial medical science, with all its benefits and risks, is being integrated into local health systems and emerging drug markets.
Providing a unique perspective on globalized clinical trials,When Experiments Travelraises central questions: Are such trials exploitative or are they social goods? How are experiments controlled and how is drug safety ensured? And do these experiments help or harm public health in the countries where they are conducted? Empirically rich and theoretically innovative, the book shows that neither the language of coercion nor that of rational choice fully captures the range of situations and value systems at work in medical experiments today.When Experiments Travelchallenges conventional understandings of the ethics and politics of transnational science and changes the way we think about global medicine and the new infrastructures of our lives.
Habitat‐selection analyses allow researchers to link animals to their environment via habitat‐selection or step‐selection functions, and are commonly used to address questions related to wildlife ...management and conservation efforts. Habitat‐selection analyses that incorporate movement characteristics, referred to as integrated step‐selection analyses, are particularly appealing because they allow modelling of both movement and habitat‐selection processes.
Despite their popularity, many users struggle with interpreting parameters in habitat‐selection and step‐selection functions. Integrated step‐selection analyses also require several additional steps to translate model parameters into a full‐fledged movement model, and the mathematics supporting this approach can be challenging for many to understand.
Using simple examples, we demonstrate how weighted distribution theory and the inhomogeneous Poisson point process can facilitate parameter interpretation in habitat‐selection analyses. Furthermore, we provide a ‘how to’ guide illustrating the steps required to implement integrated step‐selection analyses using the amt package
By providing clear examples with open‐source code, we hope to make habitat‐selection analyses more understandable and accessible to end users.
Habitat‐selection analyses allow researchers to link animals to their environment in support of wildlife management and conservation efforts. We provide a ‘how to' guide for correctly interpreting parameters in habitat‐ and step‐selection functions and for implementing integrated step‐selection analyses using the amt package for program R.
Habitat‐selection analyses are often used to link environmental covariates, measured within some spatial domain of assumed availability, to animal location data that are assumed to be independent. ...Step‐selection functions (SSFs) relax this independence assumption, by using a conditional model that explicitly acknowledges the spatiotemporal dynamics of the availability domain and hence the temporal dependence among successive locations. However, it is not clear how to produce an SSF‐based map of the expected utilization distribution. Here, we used SSFs to analyze virtual animal movement data generated at a fine spatiotemporal scale and then rarefied to emulate realistic telemetry data. We then compared two different approaches for generating maps from the estimated regression coefficients. First, we considered a naïve approach that used the coefficients as if they were obtained by fitting an unconditional model. Second, we explored a simulation‐based approach, where maps were generated using stochastic simulations of the parameterized step‐selection process. We found that the simulation‐based approach always outperformed the naïve mapping approach and that the latter overestimated home‐range size and underestimated local space‐use variability. Differences between the approaches were greatest for complex landscapes and high sampling rates, suggesting that the simulation‐based approach, despite its added complexity, is likely to offer significant advantages when applying SSFs to real data.
In the wake of Hurricane Katrina, many questioned whether the large number of political appointees in the Federal Emergency Management Agency contributed to the agency's poor handling of the ...catastrophe, ultimately costing hundreds of lives and causing immeasurable pain and suffering.The Politics of Presidential Appointmentsexamines in depth how and why presidents use political appointees and how their choices impact government performance--for better or worse.
One way presidents can influence the permanent bureaucracy is by filling key posts with people who are sympathetic to their policy goals. But if the president's appointees lack competence and an agency fails in its mission--as with Katrina--the president is accused of employing his friends and allies to the detriment of the public. Through case studies and cutting-edge analysis, David Lewis takes a fascinating look at presidential appointments dating back to the 1960s to learn which jobs went to appointees, which agencies were more likely to have appointees, how the use of appointees varied by administration, and how it affected agency performance. He argues that presidents politicize even when it hurts performance--and often with support from Congress--because they need agencies to be responsive to presidential direction. He shows how agency missions and personnel--and whether they line up with the president's vision--determine which agencies presidents target with appointees, and he sheds new light on the important role patronage plays in appointment decisions.
In recent years the American public has witnessed several hard-fought battles over nominees to the U.S. Supreme Court. In these heated confirmation fights, candidates' legal and political ...philosophies have been subject to intense scrutiny and debate.Citizens, Courts, and Confirmationsexamines one such fight--over the nomination of Samuel Alito--to discover how and why people formed opinions about the nominee, and to determine how the confirmation process shaped perceptions of the Supreme Court's legitimacy.
Drawing on a nationally representative survey, James Gibson and Gregory Caldeira use the Alito confirmation fight as a window into public attitudes about the nation's highest court. They find that Americans know far more about the Supreme Court than many realize, that the Court enjoys a great deal of legitimacy among the American people, that attitudes toward the Court as an institution generally do not suffer from partisan or ideological polarization, and that public knowledge enhances the legitimacy accorded the Court. Yet the authors demonstrate that partisan and ideological infighting that treats the Court as just another political institution undermines the considerable public support the institution currently enjoys, and that politicized confirmation battles pose a grave threat to the basic legitimacy of the Supreme Court.
Popular frameworks for studying habitat selection include resource‐selection functions (RSFs) and step‐selection functions (SSFs), estimated using logistic and conditional logistic regression, ...respectively. Both frameworks compare environmental covariates associated with locations animals visit with environmental covariates at a set of locations assumed available to the animals. Conceptually, slopes that vary by individual, that is, random coefficient models, could be used to accommodate inter‐individual heterogeneity with either approach. While fitting such models for RSFs is possible with standard software for generalized linear mixed‐effects models (GLMMs), straightforward and efficient one‐step procedures for fitting SSFs with random coefficients are currently lacking.
To close this gap, we take advantage of the fact that the conditional logistic regression model (i.e. the SSF) is likelihood‐equivalent to a Poisson model with stratum‐specific fixed intercepts. By interpreting the intercepts as a random effect with a large (fixed) variance, inference for random‐slope models becomes feasible with standard Bayesian techniques, or with frequentist methods that allow one to fix the variance of a random effect. We compare this approach to other commonly applied alternatives, including models without random slopes and mixed conditional regression models fit using a two‐step algorithm.
Using data from mountain goats (Oreamnos americanus) and Eurasian otters (Lutra lutra), we illustrate that our models lead to valid and feasible inference. In addition, we conduct a simulation study to compare different estimation approaches for SSFs and to demonstrate the importance of including individual‐specific slopes when estimating individual‐ and population‐level habitat‐selection parameters.
By providing coded examples using integrated nested Laplace approximations (INLA) and Template Model Builder (TMB) for Bayesian and frequentist analysis via the R packages R‐INLA and glmmTMB, we hope to make efficient estimation of RSFs and SSFs with random effects accessible to anyone in the field. SSFs with individual‐specific coefficients are particularly attractive since they can provide insights into movement and habitat‐selection processes at fine‐spatial and temporal scales, but these models had previously been very challenging to fit.
The authors provide a coherent framework for fitting resource‐selection functions (RSFs) and step‐selection functions (SSFs) with random effects. To allow fitting of SSFs, the authors reformulate the conditional logistic regression model as a (likelihood‐equivalent) Poisson model, where stratum‐specific intercepts are included as a random effect with a fixed large prior variance.
All of life is a game, and evolution by natural selection is no exception. The evolutionary game theory developed in this 2005 book provides the tools necessary for understanding many of nature's ...mysteries, including co-evolution, speciation, extinction and the major biological questions regarding fit of form and function, diversity, procession, and the distribution and abundance of life. Mathematics for the evolutionary game are developed based on Darwin's postulates leading to the concept of a fitness generating function (G-function). G-function is a tool that simplifies notation and plays an important role developing Darwinian dynamics that drive natural selection. Natural selection may result in special outcomes such as the evolutionarily stable strategy (ESS). An ESS maximum principle is formulated and its graphical representation as an adaptive landscape illuminates concepts such as adaptation, Fisher's Fundamental Theorem of Natural Selection, and the nature of life's evolutionary game.
In management, decisions are expected to be based on rational analytics rather than intuition. But intuition, as a human evolutionary achievement, offers wisdom that, despite all the advances in ...rational analytics and AI, should be used constructively when recruiting and winning personnel. Integrating these inner experiential competencies with rational-analytical procedures leads to smart recruiting decisions.
Advances in tracking technology have led to an exponential increase in animal location data, greatly enhancing our ability to address interesting questions in movement ecology, but also presenting ...new challenges related to data management and analysis. Step‐selection functions (SSFs) are commonly used to link environmental covariates to animal location data collected at fine temporal resolution. SSFs are estimated by comparing observed steps connecting successive animal locations to random steps, using a likelihood equivalent of a Cox proportional hazards model. By using common statistical distributions to model step length and turn angle distributions, and including habitat‐ and movement‐related covariates (functions of distances between points, angular deviations), it is possible to make inference regarding habitat selection and movement processes or to control one process while investigating the other. The fitted model can also be used to estimate utilization distributions and mechanistic home ranges. Here, we present the R package amt (animal movement tools) that allows users to fit SSFs to data and to simulate space use of animals from fitted models. The amt package also provides tools for managing telemetry data. Using fisher (Pekania pennanti) data as a case study, we illustrate a four‐step approach to the analysis of animal movement data, consisting of data management, exploratory data analysis, fitting of models, and simulating from fitted models.
New tracking technologies allow users to collect large amount of data and address entirely new questions. The amt (animal movement tools) R package provides tools to manage telemetry data and to fit step‐selection functions and resource‐selection functions.
Managing a high-growth organization requires both strategy and
adaptability. Unfortunately, start-up founders and executives
seeking to scale up to the next level find all too frequently that
growth ...turns into chaos. Rather than laying the groundwork for the
future, organizations get stuck by covering up complex problems
with unsustainable band-aids and duct-tape fixes, implementing
anecdote-based solutions from the latest tech-industry unicorns or
leadership books, and relying on too much on-the-fly learning from
inexperienced managers. This book is the definitive guide for
leaders of high-growth organizations seeking to understand and
execute the people-management principles that are essential to
continued success. Combining a wealth of practical experience,
well-grounded academic research, and easy-to-apply frameworks,
Andrew Bartlow and T. Brad Harris offer a practical toolkit that
founders, functional leaders, and managers of people can use to
rethink their practices to meet their organizations' needs. They
help readers identify the core people-management programs and
practices that are best for an organization at its current stage
and size while also supporting a foundation for continued
development and the capacity to adapt to inevitable surprises.
Practical, actionable, and supplemented with numerous diagnostic
tools and illustrative examples, Scaling for Success is a
must-have playbook for organizational leaders pursuing smart and
sustainable growth.
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