|dc.contributor.author||Doran, Yaegan John||-|
|dc.description.abstract||This thesis explores the nature of knowledge in physics and the discourse that organises it. In particular, it focuses on the affordances of mathematics, image and language for construing the highly technical meanings that constitute this knowledge. It shows that each of these resources play a crucial role in physics’ ability to generate generalised theory whilst maintaining relevance to the empirical physical world.
First, to understand how mathematics contributes to knowledge-building, the thesis presents a detailed descriptive model from the perspective of Systemic Functional Semiotics that considers mathematics on its own terms. The description builds on O’Halloran’s (2005) grammar in order to understand mathematics’ intrinsic functionality and theoretical architecture. In doing so, it takes an axial perspective (Martin 2013) that considers the paradigmatic and syntagmatic axes in Systemic Functional theory as the theoretical primitives from which metafunction, strata, rank and all other theoretical categories can be derived. It shows that, when not transposing categories from English but rather deriving them from axial principles, mathematics’ theoretical architecture is considerably different to that of any resource previously seen. Looking metafunctionally, mathematics displays a highly elaborated logical component within the ideational metafunction, but shows no evidence for a discrete interpersonal metafunction. Looking at the levels within the grammar, it displays two interacting hierarchies: a rank scale based on constituency and a nesting scale based on iterative layering. Finally, it shows distinct and predictable texts patterns in its interaction with language. From this, the description is able to use genre as a unifying semiotic that strongly predicts the grammatical patterns that occur throughout physics discourse. By developing these models, the thesis offers an understanding of mathematics’ unique functionality and the reasons it is consistently used in physics.
Second, the thesis interprets the images of physics from the perspective of the Systemic Functional dimension of field. It shows that much of the power of images comes from the large number of distinct meanings that can be encapsulated in a single snapshot. In one image, large taxonomies, long sequences of activity, extensive arrays of data and various levels of specificity can all be presented. This allows various components of physics’ knowledge to be related and coordinated, and aids physics in building a coherent and integrated knowledge structure.
Following the descriptive component of the thesis, the specific functionalities of mathematics, image and language are interpreted through the Legitimation Code Theory dimension of Semantics. This provides an understanding of the organisation of physics’ knowledge structure as a whole. It shows how the interaction of mathematics, language and image underpins physics’ ability to progressively build ever more elaborated technical meanings, to make empirical predictions from theoretical models and to abstract theoretical generalisations from empirical data. By interpreting the mathematics, image and language used in physics from the complementary perspectives of Systemic Functional Semiotics and Legitimation Code Theory, the thesis offers a detailed model of how physics manages to make sense of and predict the vast physical world.||en_AU|
|dc.publisher||University of Sydney||en_AU|
|dc.publisher||Faculty of Arts and Social Sciences||en_AU|
|dc.publisher||School of Literature, Art, and Media||en_AU|
|dc.publisher||Department of Linguistics||en_AU|
|dc.subject||Systemic functional linguistics||en_AU|
|dc.subject||Legitimation Code Theory||en_AU|
|dc.title||Knowledge in Physics through Mathematics, Image and Language||en_AU|
|dc.type.pubtype||Doctor of Philosophy Ph.D.||en_AU|
|Appears in Collections:||Sydney Digital Theses (Open Access)|