Bibliographic record
Abstract
The digital transformation of marketing has been ongoing for more than three decades but the breadth and depth of change in the last five years has been unprecedented. We know from extensive research on identities in organisations that change in work practices can prompt identity work, yet there has been relatively little prior research about marketer identities. Moreover, there has been even less research about marketer identities relating to digital transformation. This thesis addresses these gaps; however, it does so by looking at the intersection of marketer identities and digital transformation via a Pragmatist reading of Holland et al.’s (1998) concept of Figured Worlds, a social practice theory of identity with roots in Vygotsky, Bakhtin, Mead, and Bourdieu. This approach enabled the study of processes of transformation in relation to the various artefacts which make up figured worlds, such as vocabularies, practices, and materialities which come together to construct understandings about ‘how things work’ or what is considered ‘normal’ by the people who inhabit them. The main body of the thesis centres on an ethnographically-oriented case study of the marketing department of a large Canadian NGO (Canango) in the process of shifting from a traditional ‘NGO helper’ culture to a so-called ‘Agile marketing’ culture based on project management practices originating in software development that have been growing in popularity among practitioners. The thesis identifies a number of ‘classes’ of marketer identities: managerially supplied ; technologically afforded ; socially afforded ; emergent ; and, performed along with what each type enables one to do. Using ideas from Figured Worlds theory and multimodal discourse analysis, a heuristic framework is then developed made of the elements ‘ matter ’ (phenomena), ‘ meaning ’, mediators , ‘ me ’ (identity) and ‘ motion ’ (action) to study how these identities are used to accomplish contextual goals. This framework is then applied to study the way that three people variously appropriated or resisted a particular supplied identity: the ‘Agile organiser’. Finally the ideas developed through the first three phases of the thesis are applied in a final phase in which Canango begins using a new digital collaborative work platform. The study looks at the identity implications of this move, evidencing the ways in which the work platform serves as a ‘bridge’ between worlds and how such bridges may be used to change worlds and make new ones.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.037 |
| Scholarly communication | 0.019 | 0.020 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".