Perennial interest in description and discussion of animals has been given a new edge by environmental changes. Global warming, threats to ecosystems, loss of biodiversity and pressure on food supplies require new kinds of understanding and action. Using an innovative and ambitious corpus-assisted discourse analytic methodology to investigate the language, production and reception of texts about animals, this project examines how language choices realise specific stances toward animals (e.g. as surrogate humans, objects of affection, industrial products, experimental models or pests).
We have collected extensive empirical data encompassing a wide range of discourse about animals, produced between 1995 and 2015, and are investigating how linguistic choices within that data relate to particular scientific, philosophical, ethical, popular and practical stances towards them.
Our approach is discourse analytic in that we recognise that meaning does not reside solely in the text, but arises in interaction between specific participants in particular contexts. It is to enable insight into such interaction that we are correlating text analysis with interviews and focus groups.
Our approach is also applied linguistic in that it is concerned with the investigation of real-world problems in which language is a central issue. The real-world problems we are addressing include: the potential incommensurability of different discourses (e.g. scientific, religious, economic) and resulting misunderstandings; the divide between popular and scientific conceptions of animals; the difficulty which many experts face when trying to reconcile accuracy and accessibility; and the mutual antipathy of different ethical stances such as advocacy of animal rights and animal experimentation.
The corpus-assisted discourse analytic methodology aims to create an overview of the many ways in which animals feature in human practices, and how the views of those communicating professionally about animals relate to the language they use and its effects. We are correlating corpus linguistic analysis with qualitative analysis of the three kinds of data under investigation, namely data from texts about animals, text producers and text receivers.
In addition, insofar as we not only consider (i) what is said about animals but also (ii) who says it, and (iii) to whom, the project will also contribute to advancing the triangle of communication methodology developed by Professor Guy Cook (discussed in Genetically Modified Language: The Discourse of Arguments for GM Crops and Food).
Our data is of three main kinds, encompassing:
1. The representation of animals in discourse
We have collected a large digital database of texts (both writing and transcribed speech) about animals drawn from sources such as newspapers, wildlife broadcasts, campaigning literature, food product labels, etc. This corpus is being analysed using specialised linguistic software to reveal frequent patterns of language use.
2. The production of discourse about animals
Secondly, we have conducted and transcribed a series of interviews with some producers of such texts, again drawn from a wide range of interests and practices in relation to animals: broadcasters, scientists, environmentalists, animal welfare campaigners, farmers’ representatives and others, to elicit their views on the best language to achieve their purposes.
3. The reception of that discourse
The third dataset is made up of transcriptions of focus groups, some with various interest groups, others with members of the general public. This dataset aims to ascertain participants’ responses to a series of selected texts from the corpus. In this way we will be able to correlate analysis of texts about animals with insights into their production and reception, thus avoiding the danger of over reliance on textual analysis alone. At the same time, the transcribed spoken data from the interviews and focus groups will itself be added to the digital corpus of discourse about animals for additional linguistic analysis.
Each dataset will be used to address distinct research questions and the analytical method used will reveal the inter-relationships between these three types of data.