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Institution

Deakin University

EducationBurwood, Victoria, Australia
About: Deakin University is a education organization based out in Burwood, Victoria, Australia. It is known for research contribution in the topics: Population & Context (language use). The organization has 12118 authors who have published 46470 publications receiving 1188841 citations. The organization is also known as: Deakin.


Papers
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Book ChapterDOI
01 Jan 2014
TL;DR: It is demonstrated how PGLS can incorporate information about phylogenetic signal, the extent to which closely related species truly are similar, and how it controls for this signal appropriately, thereby negating concerns about unnecessarily ‘correcting’ for phylogeny.
Abstract: Phylogenetic generalised least squares (PGLS) is one of the most commonly employed phylogenetic comparative methods. The technique, a modification of generalised least squares, uses knowledge of phylogenetic relationships to produce an estimate of expected covariance in cross-species data. Closely related species are assumed to have more similar traits because of their shared ancestry and hence produce more similar residuals from the least squares regression line. By taking into account the expected covariance structure of these residuals, modified slope and intercept estimates are generated that can account for interspecific autocorrelation due to phylogeny. Here, we provide a basic conceptual background to PGLS, for those unfamiliar with the approach. We describe the requirements for a PGLS analysis and highlight the packages that can be used to implement the method. We show how phylogeny is used to calculate the expected covariance structure in the data and how this is applied to the generalised least squares regression equation. We demonstrate how PGLS can incorporate information about phylogenetic signal, the extent to which closely related species truly are similar, and how it controls for this signal appropriately, thereby negating concerns about unnecessarily ‘correcting’ for phylogeny. In addition to discussing the appropriate way to present the results of PGLS analyses, we highlight some common misconceptions about the approach and commonly encountered problems with the method. These include misunderstandings about what phylogenetic signal refers to in the context of PGLS (residuals errors, not the traits themselves), and issues associated with unknown or uncertain phylogeny.

328 citations

Journal ArticleDOI
TL;DR: This critical analysis offers new strategies to limit the number of nano/microplastics in water and wastewater to keep water quality up to the required standards and reduce threats on the authors' ecosystems.

327 citations

Journal ArticleDOI
TL;DR: A comparative study of the effectiveness of Y, La, Ce and Gd as texture modifiers during the extrusion of magnesium-based alloys has been carried out in this paper, where it was found that Y was not as effective as the other three elements in modifying the texture, and at no concentration studied did this element form a typical rare earth texture.
Abstract: A comparative study of the effectiveness of Y, La, Ce and Gd as texture modifiers during the extrusion of magnesium-based alloys has been carried out. It was found that La, Ce and Gd are all effective texture modifiers, being able to produce the “rare earth” texture at the low alloying levels of 300, 400 and 600 ppm respectively. Y was not as effective as the other three elements in modifying the texture, and at no concentration studied did this element form a typical “rare earth” texture. It is proposed that a strong interaction of solutes with dislocations and grain boundaries is responsible for the significant impact rare earth additions have on the extruded grain size and texture at very low alloying levels.

326 citations

Journal ArticleDOI
TL;DR: In this article, the authors present a framework that can be used to integrate CSR into business processes, highlighting the concept of simultaneous top-down integration and bottom-up community-related indicators development.

326 citations

Journal ArticleDOI
TL;DR: Green human resource management (GHRM) as mentioned in this paper is an emergent field of research in the field of human resources management (HRM), which has been studied extensively in the last few decades.
Abstract: The growing awareness of and regulations related to environmental sustainability have invoked the concept of green human resource management (GHRM) in the search for effective environmental management (EM) within organizations. GHRM research raises new, increasingly salient questions not yet studied in the broader human resource management (HRM) literature. Despite an expansion in the research linking GHRM with various aspects of EM and overall environmental performance, GHRM’s theoretical foundations, measurement, and the factors that give rise to GHRM (including when and how it influences outcomes) are still under-specified. This paper, seeking to better understand research opportunities and advance theoretical and empirical development, evaluates the emergent academic field of GHRM with a narrative review. This review highlights an urgent need for refined conceptualization and measurement of GHRM and develops an integrated model of the antecedents, consequences and contingencies related to GHRM. Going beyond a function-based perspective that focuses on specific HRM practices and building on advances in the strategic HRM literature, we discuss possible multi-level applications, the importance of employee perceptions and experiences related to GHRM, contextual and cultural implications, and alternative theoretical approaches. The detailed and focused review provides a roadmap to stimulate the development of the GHRM field for scholars and practicing managers.

326 citations


Authors

Showing all 12448 results

NameH-indexPapersCitations
Patrick D. McGorry137109772092
Mary Story13552264623
Dacheng Tao133136268263
Paul Harrison133140080539
Paul Zimmet128740140376
Neville Owen12770074166
Louisa Degenhardt126798139683
David Scott124156182554
Anthony F. Jorm12479867120
Tao Zhang123277283866
John C. Wingfield12250952291
John J. McGrath120791124804
Eduard Vieta119124857755
Michael Berk116128457743
Ashley I. Bush11656057009
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023162
2022677
20215,124
20204,513
20193,981
20183,543