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1. |
Record Nr. |
UNINA9910794560503321 |
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Autore |
Kokkos Alexis |
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Titolo |
Exploring art for perspective transformation / / Alexis Kokkos |
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Pubbl/distr/stampa |
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Leiden, Netherlands ; ; Boston, Massachusetts : , : Brill Sense, , [2021] |
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©2021 |
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ISBN |
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Descrizione fisica |
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1 online resource (255 pages) |
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Collana |
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International Issues in Adult Education |
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Disciplina |
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Soggetti |
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Arts in education |
Critical pedagogy |
Transformative learning |
Learning - Philosophy |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references and indexes. |
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Nota di contenuto |
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The distinctive nature of Learning for Change -- Cognitive theory of art -- Aristotle's poetics -- The views of John Dewey and Maxine Greene -- The perspective of Frankfurt School -- The legacy of Freire and Gramsci -- Alternative approaches -- The theoretical foundations and principles of the method -- The stages of TLAE method -- Examples of implementation -- Inferences drawn from application -- Concluding reflections. |
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Sommario/riassunto |
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"We live in a socio-cultural reality which is dominated by an entrepreneurial and instrumental rationality, as well as by a discriminative and populist mentality. Questioning the validity of taken-for-granted sovereign perspectives is thus of vital importance. Our contact with art can serve as a pathway through which we might be empowered to identify false life values and develop the disposition and ability to challenge them. The learning potential of aesthetic experience is, however, barely exploited within educational systems. In addition, although major scholars have contributed to a deeper understanding of the liberating dimension of processing important artworks, there has been surprisingly little discussion in the relevant literature focusing on educational practice. Exploring Art for Perspective Transformation provides a comprehensive analysis and synthesis of theoretical views |
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pertaining to the emancipatory process of exploring art. Moreover, it presents the educational method Transformative Learning through Aesthetic Experience (TLAE), with reference to particular examples of implementation. TLAE is addressed to adult educators and school teachers regardless of the subject they teach and their theoretical background on aesthetics. It involves engaging learners in exploring works from fine arts, literature, theatre, cinema and music with a view to promoting critical reflection on one's potentially problematic perspectives"-- |
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2. |
Record Nr. |
UNINA9911020330003321 |
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Autore |
Lawson Andrew (Andrew B.) |
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Titolo |
Disease mapping with WinBUGS and MLwiN / / Andrew B. Lawson, William J. Browne, Carmen L. Vidal Rodeiro |
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Pubbl/distr/stampa |
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Chichester, West Sussex, England ; ; Hoboken, NJ, : J. Wiley, c2003 |
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ISBN |
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9786610270392 |
9781280270390 |
128027039X |
9780470341643 |
0470341645 |
9780470856055 |
047085605X |
9780470856062 |
0470856068 |
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Descrizione fisica |
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1 online resource (293 p.) |
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Collana |
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Altri autori (Persone) |
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BrowneWilliam J <1972-> (William John) |
Vidal RodeiroCarmen L |
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Disciplina |
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Soggetti |
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Medical mapping |
Medical geography - Maps - Data processing |
Epidemiology - Statistical methods |
Epidemiology - Data processing |
Public health surveillance |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references (p. 267-273) and index. |
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Nota di contenuto |
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Disease Mapping with WinBUGS and MLwiN; Contents; Preface; Notation; 0.1 Standard notation for multilevel modelling; 0.2 Spatial multiple-membership models and the MMMC notation; 0.3 Standard notation for WinBUGS models; 1 Disease mapping basics; 1.1 Disease mapping and map reconstruction; 1.2 Disease map restoration; 2 Bayesian hierarchical modelling; 2.1 Likelihood and posterior distributions; 2.2 Hierarchical models; 2.3 Posterior inference; 2.4 Markov chain Monte Carlo methods; 2.5 Metropolis and Metropolis-Hastings algorithms; 2.6 Residuals and goodness of fit; 3 Multilevel modelling |
3.1 Continuous response models3.2 Estimation procedures for multilevel models; 3.3 Poisson response models; 3.4 Incorporating spatial information; 3.5 Discussion; 4 WinBUGS basics; 4.1 About WinBUGS; 4.2 Start using WinBUGS; 4.3 Specification of the model; 4.4 Model fitting; 4.5 Scripts; 4.6 Checking convergence; 4.7 Spatial modelling: GeoBUGS; 4.8 Conclusions; 5 MLwiN basics; 5.1 About MLwiN; 5.2 Getting started; 5.3 Fitting statistical models; 5.4 MCMC estimation in MLwiN; 5.5 Spatial modelling; 5.6 Conclusions; 6 Relative risk estimation; 6.1 Relative risk estimation using WinBUGS |
6.2 Spatial prediction6.3 An analysis of the Ohio dataset using MLwiN; 7 Focused clustering: the analysis of putative health hazards; 7.1 Introduction; 7.2 Study design; 7.3 Problems of inference; 7.4 Modelling the hazard exposure risk; 7.5 Models for count data; 7.6 Bayesian models; 7.7 Focused clustering in WinBUGS; 7.8 Focused clustering in MLwiN; 8 Ecological analysis; 8.1 Introduction; 8.2 Statistical models; 8.3 WinBUGS analyses of ecological datasets; 8.4 MLwiN analyses of ecological datasets; 9 Spatially-correlated survival analysis; 9.1 Survival analysis in WinBUGS |
9.2 Survival analysis in MLwiN10 Epilogue; Appendix 1: WinBUGS code for focused clustering models; A.1 Falkirk example; A.2 Ohio example; Appendix 2: S-Plus function for conversion to GeoBUGS format; Bibliography; Index |
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Sommario/riassunto |
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Disease mapping involves the analysis of geo-referenced disease incidence data and has many applications, for example within resource allocation, cluster alarm analysis, and ecological studies. There is a real need amongst public health workers for simpler and more efficient tools for the analysis of geo-referenced disease incidence data. Bayesian and multilevel methods provide the required efficiency, and with the emergence of software packages - such as WinBUGS and MLwiN - are now easy to implement in practice.Provides an introduction to Bayesian and multilevel modelling in disease m |
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