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The Digital Score Methodology

A methodology is a system of methods used to generate critical data that can lead to new insights and defined knowledge of a research project. This methodology is specific to the case study work package, and the aim is:

To analyse in-vivo experiences of creativity and musicking in digital scores underpinned with the 7 tech themes of artificial intelligence, machine learning, internet networking, robotics, virtual and augmented reality, gaming and physical computing

As DigiScore case studies are practice-based, a bespoke methodology is needed, as no out-of-the-box one exists or meets the particular needs of the work package. Over the course of the first year of the project, we designed a methodology comprising a research strategy and the identification of procedures, tools and techniques to be used.

Key points

-practice is not research; practice is a site in/ with/ through which the research is conducted. (This is a common misconception. For more information read Vear, C (2022) ed The Routledge International Handbook of Practice-Based Research: London, Routledge)

-methodology is built around the expanded theoretical framework (see below) and key research questions and packaged as a dataset (see below)

-there are three parts to data collection: intention (creation), the digital score, and reception(realisation)

-in every stage of data collection, we will collect musicians’ reflections in the process (in-vivo) and on the process (in-vitro)

-data on process & reflection, creation & realisation are important aspects of the methodology

Dataset Design

The data collected from every digital score case study conforms to a dataset designed to capture musicians’ experiences in, with or through musicking. It has been designed to capture over two discreet phases of each digital score project a) the intention phase (also called creation or encoding), and b) the reception phase (also called decoding or realisation) (see fig. below).

The methods/ tools applied for each of these phases were chosen to capture experiences from two different perspectives using an innovative approach of: 1) In-vivo (articulating experiences inside the creative acts), 2) In-vitro (reflecting on inside experiences from outside the creative acts).

The data collection starts with the initial project proposal/ artistic intent statement by the composer/maker and ends with questionnaires on the legacy of the experience with the digital score. The dataset includes:

INTENTION PHASE

  • the artistic proposal from the composer: this is designed to outline the aesthetic and technical design intention of the project. It was acknowledged that this might change through the process of creativity but is an important document to capture in order to book-end the creative process. (In-vitro)
  • Reflective journal/ digital blog: designed to capture ongoing thoughts and reflection on the creative process. (In-Vivo)
  • Intention Statement questionnaires from all creators: designed to articulate the intended connections and relationships embedded into the digital score as they relate to the theoretical framework.

THE ARTEFACT

  • The digital score, and its materials, documentation, code, instructions, media file etc.

RECEPTION PHASE

  • video documentation of the performances
  • stimulated recall interviews: with the “performers” immediately following their performance
  • semi-structured interviews with the “performers”: conducted as soon as possible, on the same day or within the next couple of days of the performance
  • audience surveys from the first performance: gathered immediately following the performance
  • semi-structured interview from the “composer”: conducted within a week following the performance focussing on their reception of the realisation of the digital score
  • legacy questionnaires from “performers”: gathered after 4 weeks from the performance and focus on the lasting legacy of the experience of the performance and the digital score: one from each “performer”
  • legacy questionnaires from the creators: gathered after 4 weeks from the performance and focus on the lasting legacy of the experience of the compositional process with the digital score: one from each creator

The Theoretical Framework

The Digital Score project methodology will use a theoretical framework that builds on existing research to investigate how digital scores can stimulate new relationships between musicians, open up the possibilities of novel creative experiences, and how these experiences can profoundly influence the nature of digital musicianship.

The Digital Score theoretical framework is based on the idea that music is something that happens between people (Small, 1998), and that meaning is made through our relationships with new creative acts of musicking and technologies of digital scores.

In the theoretical framework, we also build on Craig Vear’s previous research from The digital score: Musicianship, creativity and innovation (2019). From here we take the notion of Taking In and Taken Into:

  • Taking-in: how the perceived affect of the technology and media of a digital score is reaching out, suggesting, offering and shifting through the tendrils of affordance and experience and makes connections with the musician(s) through notions of a) Liveness, b) Presence, and c) Interaction.
  • Taken into: how the digital score can establish a world of creative possibilities through embodiment and the flow of the domains of a) Play, b) Time, and c) Sensation.

In addition, the methodology of the Digital Score project will use a theoretical framework that includes the 4Es of embodied music cognition, gaming theory, McLuhan’s The Medium is the Message and theories of flat ontology (Bogost 2012) to understand how musicians experience flow while interacting with digital scores.

Methods of Data Analysis

The raw data that we use for data analysis are the questionnaires, semi-structured interviews, stimulated recall transcripts and blog data from the dataset (see diagram above). We used two approaches when analysing the data:

  1. Thematic coding of the raw data using Nvivo software aligned to the theoretical framework (a top-down approach)
  2. A grounded theory method[1] uses an open and iterative approach to examining the data from which new themes can emerge (a bottom-up approach)

As discussed above, the theoretical framework guides the questioning process and the analysis of the dataset while aligning with the core aims of the scientific investigation. The purpose of the theoretical framework is to draw out new insights into the relationships formed with digital score musicking. They are categorised into three different themes. These are:

  1. musicians’ connectivity to the materials of the digital score; and the experience of their flow in the moment of interpretation (encompassing themes 1 and 2 from the framework)
  2. the digital skills needed and acquired from making and interacting with the digital score (theme 3 from the framework)
  3. the transformative experiences musicians had with the digital scores that might have an impact on their future creativity and music-making experiences (theme 4 from the framework)

Some Examples of analysed case studies and their datasets (accessed on figshare):

Nautilus https://digiscore.dmu.ac.uk/2022/01/27/nautilus/

DigiScore TENOR22 commissions https://digiscore.dmu.ac.uk/2022/10/05/digiscore-tenor22-commissions/

Pathways by Dejana Sekulic https://digiscore.dmu.ac.uk/2022/10/11/pathways-by-dejana-sekulic/


[1] Glaser, B. (1998). Doing Grounded Theory: Issues and Discussions. Mill Valley, CA: Sociology Press.

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