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Historia ludens : the playing historian
\"This book aims to further a debate about aspects of 'playing' and 'gaming' in connection with history. Reaching out to academics, professionals and students alike, it pursues a dedicated interdisciplinary approach. Rather than only focusing on how professionals could learn from academics in history, the book also ponders the question of what academics can learn from gaming and playing for their own practice, such as gamification for teaching, or using 'play' as a paradigm for novel approaches into historical scholarship. 'Playing' and 'gaming' are thus understood as a broad cultural phenomenon that cross-pollinates the theory and practice of history and gaming alike\"-- Provided by publisher.
Book
Simulation Study of Surveillance Strategies for Faster Detection of Novel SARS-CoV-2 Variants
Earlier global detection of novel SARS-CoV-2 variants gives governments more time to respond. However, few countries can implement timely national surveillance, resulting in gaps in monitoring. The United Kingdom implemented large-scale community and hospital surveillance, but experience suggests it might be faster to detect new variants through testing England arrivals for surveillance. We developed simulations of emergence and importation of novel variants with a range of infection hospitalization rates to the United Kingdom. We compared time taken to detect the variant though testing arrivals at England borders, hospital admissions, and the general community. We found that sampling 10%–50% of arrivals at England borders could confer a speed advantage of 3.5–6 weeks over existing community surveillance and 1.5–5 weeks (depending on infection hospitalization rates) over hospital testing. Directing limited global capacity for surveillance to highly connected ports could speed up global detection of novel SARS-CoV-2 variants.
Journal Article
Can You Beat Churchill?
How do you get students to engage in a historical
episode or era? How do you bring the immediacy and
contingency of history to life? Michael A. Barnhart shares the
secret to his award-winning success in the classroom with Can
You Beat Churchill? , which encourages role-playing for
immersive teaching and learning. Combating the declining enrollment
in humanities classes, this innovative approach reminds us how
critical learning skills are transmitted to students: by
reactivating their curiosity and problem-solving abilities.
Barnhart provides advice and procedures, both for the use of
off-the-shelf commercial simulations and for the instructor who
wishes to custom design a simulation from scratch. These
reenactments allow students to step into the past, requiring them
to think and act in ways historical figures might have. Students
must make crucial or dramatic decisions, though these decisions
need not align with the historical record. In doing so, they learn,
through action and strategic consideration, the impact of real
individuals and groups of people on the course of history.
There is a quiet revolution underway in how history is taught to
undergraduates. Can You Beat Churchill? hopes to make it a
noisy one.
eBook
Magnetosphere Dynamics During the 14 November 2012 Storm Inferred from TWINS, AMPERE, Van Allen Probes, and BATS-R-US-CRCM
During the 14 November 2012 geomagnetic storm, the Van Allen Probes spacecraft observed a number of sharp decreases ('dropouts') in particle fluxes for ions and electrons of different energies. In this paper, we investigate the global magnetosphere dynamics and magnetosphere- ionosphere (M-I) coupling during the dropout events using multipoint measurements by Van Allen Probes, TWINS, and AMPERE together with the output of the two-way coupled global BATS-R-US-CRCM model. We find different behavior for two pairs of dropouts. For one pair, the same pattern was repeated: (1) weak nightside Region 1 and 2 Birkeland currents before and during the dropout; (2) intensification of Region 2 currents after the dropout; and (3) a particle injection detected by TWINS after the dropout. The model predicted similar behavior of Birkeland currents. TWINS low-altitude emissions demonstrated high variability during these intervals, indicating high geomagnetic activity in the near-Earth tail region. For the second pair of dropouts, the structure of both Birkeland currents and ENA emissions was relatively stable. The model also showed quasi-stationary behavior of Birkeland currents and simulated ENA emissions with gradual ring current buildup. We confirm that the first pair of dropouts was caused by large-scale motions of the OCB (open-closed boundary) during substorm activity. We show the new result that this OCB motion was associated with global changes in Birkeland (M-I coupling) currents and strong modulation of low-altitude ion precipitation. The second pair of dropouts is the result of smaller OCB disturbances not related to magnetospheric substorms. The local observations of the first pair of dropouts result from a global magnetospheric reconfiguration, which is manifested by ion injections and enhanced ion precipitation detected by TWINS and changes in the structure of Birkeland currents detected by AMPERE. This study demonstrates that multipoint measurements along with the global model results enable the reconstruction of a more complete system-level picture of the dropout events and provides insight into M-I coupling aspects that have not previously been investigated.
Journal Article
Questions of value, questions of magnitude: An exploration and application of methods for comparing indirect effects in multiple mediator models
Mediation analysis is widely used to test and inform theory and debate about the mechanism(s) by which causal effects operate, quantitatively operationalized as an
indirect effect
in a mediation model. Most effects operate through multiple mechanisms simultaneously, and a mediation model is likely to be more realistic when it is specified to capture multiple mechanisms at the same time with the inclusion of more than one mediator in the model. This also allows an investigator to compare indirect effects to each other. After an overview of the mechanics of mediation analysis, we advocate formally comparing indirect effects in models that include more than one mediator, focusing on the important distinction between questions and claims about
value
(i.e., are two indirect effects the same number?) versus
magnitude
(i.e., are two indirect effects equidistant from zero or the same in strength?). After discussing the shortcomings of the conventional method for comparing two indirect effects in a multiple mediator model—which only answers a question about magnitude in some circumstances—we introduce several methods that, unlike the conventional approach, always answer questions about difference in magnitude. We illustrate the use of these methods and provide code that implements them in popular software. We end by summarizing simulation findings and recommending which method(s) to prefer when comparing like- and opposite-signed indirect effects.
Journal Article
Generalized Network Psychometrics: Combining Network and Latent Variable Models
We introduce the network model as a formal psychometric model, conceptualizing the covariance between psychometric indicators as resulting from pairwise interactions between observable variables in a network structure. This contrasts with standard psychometric models, in which the covariance between test items arises from the influence of one or more common latent variables. Here, we present two generalizations of the network model that encompass latent variable structures, establishing network modeling as parts of the more general framework of structural equation modeling (SEM). In the first generalization, we model the covariance structure of latent variables as a network. We term this framework latent network modeling (LNM) and show that, with LNM, a unique structure of conditional independence relationships between latent variables can be obtained in an explorative manner. In the second generalization, the residual variance–covariance structure of indicators is modeled as a network. We term this generalization residual network modeling (RNM) and show that, within this framework, identifiable models can be obtained in which local independence is structurally violated. These generalizations allow for a general modeling framework that can be used to fit, and compare, SEM models, network models, and the RNM and LNM generalizations. This methodology has been implemented in the free-to-use software package
lvnet
, which contains confirmatory model testing as well as two exploratory search algorithms: stepwise search algorithms for low-dimensional datasets and penalized maximum likelihood estimation for larger datasets. We show in simulation studies that these search algorithms perform adequately in identifying the structure of the relevant residual or latent networks. We further demonstrate the utility of these generalizations in an empirical example on a personality inventory dataset.
Journal Article