Refining the instrument

By means of an analytical process involving constant comparison across instances in the different broad disciplinary fields, the concepts were refined and for each of the knowledge modalities two modes were developed. Refining the instrument in this way required a constant and active flow between literature, theory and data, employing “imagination” and “intellectual craftsmanship” (Clegg, 2012, p. 407, quoting Mills (1959)). These scholars use ‘crafting’ in the sense that the theoretical categories become conceptual ‘tools’ to think with. The literature, theory and data get assimilated in the researcher’s ‘life experience’ and it is this that is drawn upon in pulling together what initially are tentative theoretical hunches or categories to ‘try out’

76 on the data. The conceptual work done here does not take place in a theoretical vacuum, rather, literature from the field (the theory, philosophy and sociology of knowledge) is assimilated and drawn upon. It requires constant interpreting and re-examining of which aspects of the data are pulled in or left out as categories are delineated.

In this section I describe the development of the modes as further refinement of the knowledge modalities. In the development of the modes, the two broad disciplinary fields (engineering and science) were initially conceptualised as distinct analytical categories, but the modes generated the potential for a grading of intensity of strength across modalities (i.e. the modalities are conceptualised as continuous rather than discrete entities). This analytical approach made it possible to identify unanticipated deviations from ‘typical’ categorisation of science and engineering knowledge as polarities.

3.4.4.1 The specialisation modality

This category surfaced from the interaction with the data as described in 3.4.2-3.4.3. The way I will be using specialisation15 _{is with reference to the way some of the knowledge}

appears to engage with the fundamental values of each of the disciplinary areas suggesting separate societal roles for the disciplines. The analytical task for data generation and analysis therefore involves identifying data instances where certain forms of knowledge are valued or prioritised over other forms. This implies that both engineering science knowledge as well as knowledge in science are specialised, but specialised in different ways.

The fundamental disciplinary values are respectively artefact operationalisation (for engineering) and explanation and description (for science). Visualising the specialisation modality as a continuous axis of variance, it is then reasonable to suggest the following modes of specialisation of the disciplinary thermodynamics knowledge:

a. specialisation towards particulars: knowledge is directed towards deliberate intervention via particular devices, artefacts or systems

b. specialisation towards universals: knowledge emphasises the generic form, and explanatory or predictive power brought about by generalisation (applying to more than one particular instance)

There clearly are aspects of specialisation present in normativity and idealisation (see the discussion in chapter seven later). However, the decision was made to separate the categories of

15 _{Specialisation is used elsewhere to refer to the division of cooperative labour in clearly defined societal} tasks/roles (sociology), applying general knowledge to generate propositional knowledge (logic), or in instances where a less specialised object evolves towards greater specialisation (biology).

77 specialisation, idealisation and normativity at the stage of the data analysis, keeping an open mind for condensation of the categories at a later stage in the study. The reason for this is a concern about losing richness of detail in the data, should categories be collapsed too early (working with just the two ‘lenses’ of idealisation and normativity could potentially result in a blindness to other detail). The motivation for separation of the modalities was therefore a commitment to potential analytical and cognitive gain.

3.4.1.2 The idealisation modality

As discussed in chapter two, idealisation is the deliberate, selective distortion of reality for specific purposes of the modeller, in order to explain, predict or solve problems. It usually involves removing complexity (often also context), making simplifying assumptions and abstractions of real-life systems and objects. Models and idealisations are often used as epistemic tools for different purposes. In line with the fundamental values of the disciplinary fields the following modes were conceptualised for the continuum of variance in idealisation: a. idealisation towards physical realisation: this mode emphasises the task- or problem- directed nature of idealised knowledge where it is present. Zwart (2009), for example,

comments: “…in engineering the ultimate purpose of modeling is to realize reliable artifacts or technical processes” (p.633). Idealisation is used to ‘gain traction’ for the solution of a difficult problem. This potentially involves the use of approximation, clarity about the simplifying assumptions made, and possibly involves removing or compensating for the abstraction as the problem solving progresses.

b. idealisation towards abstract-ideal theorisation: here the idealised knowledge follows from or is employed in theory development. The knowledge often remains at an abstract level, and there is limited commitment to a return to a real-life context. In fact, the knowledge is often de-contextualised and general, in order to transfer across contexts.

3.4.4.3 The normativity modality

The discussion in chapter two indicated that different types of knowledge claims are made; some are simply descriptive statements about the way things are. These kinds of statements are obviously commonplace across all broad disciplinary fields as any disciplinary field of expertise will include bodies of declarative knowledge covering topics within its scope. Normativity, however, is associated with evaluative judgements being made. Radder (2009c) and others (Dancy, 2006; Franssen, 2009; Van de Poel, 2009) argue that the intentionality inherent in the production of technological artefacts brings a normative dimension to knowledge in these fields.

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Figure 3-2: The full analytical instrument

Broad disciplinary fields Nature of the knowledge

Disciplinary Field Telos Theory-calibrated orientation (concepts find their meaning as part of a theory) (to map/explain regularity) Science: Fundamental values: explain & understand the natural and modified world

Engineering:

Fundamental values: the design, construction, operation of artefacts to intervene in & modify human environment according to perceived needs Task, problem or function orientation (to map/exploit regularity to enable change) Modes Knowledge Modalities Normativity Specialisation Idealisation Knowledge Orientation Towards particulars Towards physical realisability Constitutive normativity Towards universals Towards abstract- ideal theorisation Incidental normativity To allow for the possibility of a lesser degree of normativity16 _{prevalent in the knowledge in}

some of the other disciplinary fields under consideration, the following modes were developed along the normativity continuum:

a. A normative knowledge orientation that is constitutive: here normativity plays a vital role in understanding the nature of the knowledge.

b. A normative orientation that has at most an incidental role in describing the nature of the knowledge.