De-Classrooming: Moving Learning Outside the Classroom

This paper reflects on a teaching problem highlighted as part of a second-year undergraduate module in sociology, taught at a UK based institution of higher education. The specific teaching problem – that of student learning as encountered and revealed in seminars – was nested within other issues; some of which related to the characteristics of the discipline of sociology itself, whilst others, related to more localised issues such as the choice of materials available for students to access and download. Whilst the lecture and course material was fixed, the flexibility of the seminar framework enabled the exploration and implementation of an ad hoc intervention in the form of ‘de-classrooming’. This intervention was utilised and developed to enhance the knowledge base and conceptual understanding of the student cohort in relation to “Everyday Life” sociology. The ‘de-classrooming’ intervention proved to be an efficacious pedagogic device, which facilitated dynamic levels of flexibility and creativity by both teacher and learners. As a pedagogic device, it manifested a number of key benefits: such as aiding the clarification of conceptual confusions. Ultimately, the de-classrooming intervention operated to establish an empowered sense of ownership where knowledge and knowledge-generation were concerned, and afforded students unorthodox opportunities for learning enhancement

spBayes: An R Package for Univariate and Multivariate Hierarchical Point-referenced Spatial Models

Scientists and investigators in such diverse fields as geological and environmental sciences, ecology, forestry, disease mapping, and economics often encounter spatially referenced data collected over a fixed set of locations with coordinates (latitude-longitude, Easting-Northing etc.) in a region of study. Such point-referenced or geostatistical data are often best analyzed with Bayesian hierarchical models. Unfortunately, fitting such models involves computationally intensive Markov chain Monte Carlo (MCMC) methods whose efficiency depends upon the specific problem at hand. This requires extensive coding on the part of the user and the situation is not helped by the lack of available software for such algorithms. Here, we introduce a statistical software package, spBayes, built upon the R statistical computing platform that implements a generalized template encompassing a wide variety of Gaussian spatial process models for univariate as well as multivariate point-referenced data. We discuss the algorithms behind our package and illustrate its use with a synthetic and real data example.

De-Classrooming: Moving Learning Outside the Classroom

This paper reflects on a teaching problem highlighted as part of a second-year undergraduate module in sociology, taught at a UK based institution of higher education. The specific teaching problem – that of student learning as encountered and revealed in seminars – was nested within other issues; some of which related to the characteristics of the discipline of sociology itself, whilst others, related to more localised issues such as the choice of materials available for students to access and download. Whilst the lecture and course material was fixed, the flexibility of the seminar framework enabled the exploration and implementation of an ad hoc intervention in the form of ‘de-classrooming’. This intervention was utilised and developed to enhance the knowledge base and conceptual understanding of the student cohort in relation to “Everyday Life” sociology. The ‘de-classrooming’ intervention proved to be an efficacious pedagogic device, which facilitated dynamic levels of flexibility and creativity by both teacher and learners. As a pedagogic device, it manifested a number of key benefits: such as aiding the clarification of conceptual confusions. Ultimately, the de-classrooming intervention operated to establish an empowered sense of ownership where knowledge and knowledge-generation were concerned, and afforded students unorthodox opportunities for learning enhancement

Observations on features of a research interview

This paper looks at some constitutive features of interviews and the presentation of ‘findings’ from interviews. A set of open-ended interviews was conducted after the bombing of Manchester city-centre, with residents, people who worked in the city centre, and members of the emergency services who had attended the scene. Regardless of the ‘substantive’ topic of inquiry, a naturalistic approach to the structures of talk within these interviews makes available collaborative linguistic phenomena produced by interviewer and interviewees. These routine practices included designing talk for a specific interlocutor, the use of referents or indexical expressions, the deployment of membership categories and the telling of stories. As a single case analysis, a sequence of talk from one interview is used to highlight the specificity of these ordinary features. Key words: acknowledgement tokens, Manchester bombing, Membership Categorization Analysis, ownership, recipient design, stories.Este artigo examina algumas das características constitutivas de entrevistas e da apresentação de “achados” a partir de entrevistas. Um conjunto de entrevistas abertas foram conduzidas após o ataque a bomba no centro de Manchester com residentes da área, pessoas que trabalhavam no centro da cidade e membros dos serviços de emergência que se fizeram presentes no local. Independentemente do tema “substantivo” das perguntas, um enfoque naturalista das estruturas de discurso dentro dessas entrevistas disponibiliza fenômenos lingüísticos colaborativos produzidos pelo entrevistador e pelo entrevistado. Essas práticas rotineiras incluíam a projeção do discurso para um interlocutor específico, o uso de referentes ou expressões de indexação, a distribuição em categorias de membros e a narração de histórias. Como análise de um único caso, uma seqüência de conversação tomada de uma entrevista é usada para salientar a especificidade dessas características ordinárias. Palavras-chave: sinais de reconhecimento, ataque à bomba em Manchester, Análise de Categorização de Membros, assunção, projeto de receptores, histórias

spBayes: An R Package for Univariate and Multivariate Hierarchical Point-referenced Spatial Models

Scientists and investigators in such diverse fields as geological and environmental sciences, ecology, forestry, disease mapping, and economics often encounter spatially referenced data collected over a fixed set of locations with coordinates (latitude-longitude, Easting-Northing etc.) in a region of study. Such point-referenced or geostatistical data are often best analyzed with Bayesian hierarchical models. Unfortunately, fitting such models involves computationally intensive Markov chain Monte Carlo (MCMC) methods whose efficiency depends upon the specific problem at hand. This requires extensive coding on the part of the user and the situation is not helped by the lack of available software for such algorithms. Here, we introduce a statistical software package, spBayes, built upon the R statistical computing platform that implements a generalized template encompassing a wide variety of Gaussian spatial process models for univariate as well as multivariate point-referenced data. We discuss the algorithms behind our package and illustrate its use with a synthetic and real data example

LSST: from Science Drivers to Reference Design and Anticipated Data Products

(Abridged) We describe here the most ambitious survey currently planned in the optical, the Large Synoptic Survey Telescope (LSST). A vast array of science will be enabled by a single wide-deep-fast sky survey, and LSST will have unique survey capability in the faint time domain. The LSST design is driven by four main science themes: probing dark energy and dark matter, taking an inventory of the Solar System, exploring the transient optical sky, and mapping the Milky Way. LSST will be a wide-field ground-based system sited at Cerro Pach\'{o}n in northern Chile. The telescope will have an 8.4 m (6.5 m effective) primary mirror, a 9.6 deg$^2$ field of view, and a 3.2 Gigapixel camera. The standard observing sequence will consist of pairs of 15-second exposures in a given field, with two such visits in each pointing in a given night. With these repeats, the LSST system is capable of imaging about 10,000 square degrees of sky in a single filter in three nights. The typical 5$\sigma$ point-source depth in a single visit in $r$ will be $\sim 24.5$ (AB). The project is in the construction phase and will begin regular survey operations by 2022. The survey area will be contained within 30,000 deg$^2$ with $\delta<+34.5^\circ$, and will be imaged multiple times in six bands, $ugrizy$, covering the wavelength range 320--1050 nm. About 90\% of the observing time will be devoted to a deep-wide-fast survey mode which will uniformly observe a 18,000 deg$^2$ region about 800 times (summed over all six bands) during the anticipated 10 years of operations, and yield a coadded map to $r\sim27.5$. The remaining 10\% of the observing time will be allocated to projects such as a Very Deep and Fast time domain survey. The goal is to make LSST data products, including a relational database of about 32 trillion observations of 40 billion objects, available to the public and scientists around the world.Comment: 57 pages, 32 color figures, version with high-resolution figures available from https://www.lsst.org/overvie

Assessing the carcinogenic potential of low-dose exposures to chemical mixtures in the environment: the challenge ahead.

Lifestyle factors are responsible for a considerable portion of cancer incidence worldwide, but credible estimates from the World Health Organization and the International Agency for Research on Cancer (IARC) suggest that the fraction of cancers attributable to toxic environmental exposures is between 7% and 19%. To explore the hypothesis that low-dose exposures to mixtures of chemicals in the environment may be combining to contribute to environmental carcinogenesis, we reviewed 11 hallmark phenotypes of cancer, multiple priority target sites for disruption in each area and prototypical chemical disruptors for all targets, this included dose-response characterizations, evidence of low-dose effects and cross-hallmark effects for all targets and chemicals. In total, 85 examples of chemicals were reviewed for actions on key pathways/mechanisms related to carcinogenesis. Only 15% (13/85) were found to have evidence of a dose-response threshold, whereas 59% (50/85) exerted low-dose effects. No dose-response information was found for the remaining 26% (22/85). Our analysis suggests that the cumulative effects of individual (non-carcinogenic) chemicals acting on different pathways, and a variety of related systems, organs, tissues and cells could plausibly conspire to produce carcinogenic synergies. Additional basic research on carcinogenesis and research focused on low-dose effects of chemical mixtures needs to be rigorously pursued before the merits of this hypothesis can be further advanced. However, the structure of the World Health Organization International Programme on Chemical Safety 'Mode of Action' framework should be revisited as it has inherent weaknesses that are not fully aligned with our current understanding of cancer biology