The Digital Transformation of Marketer Identities \nin Figured Worlds
Bibliographic record
Abstract
The digital transformation of marketing has been ongoing for more than three decades but the \nbreadth and depth of change in the last five years has been unprecedented. We know from \nextensive research on identities in organisations that change in work practices can prompt \nidentity work, yet there has been relatively little prior research about marketer identities. \nMoreover, there has been even less research about marketer identities relating to digital \ntransformation. This thesis addresses these gaps; however, it does so by looking at the \nintersection of marketer identities and digital transformation via a Pragmatist reading of Holland \net al.’s (1998) concept of Figured Worlds, a social practice theory of identity with roots in \nVygotsky, Bakhtin, Mead, and Bourdieu. This approach enabled the study of processes of \ntransformation in relation to the various artefacts which make up figured worlds, such as \nvocabularies, practices, and materialities which come together to construct understandings about \n‘how things work’ or what is considered ‘normal’ by the people who inhabit them. The main \nbody of the thesis centres on an ethnographically-oriented case study of the marketing \ndepartment of a large Canadian NGO (Canango) in the process of shifting from a traditional \n‘NGO helper’ culture to a so-called ‘Agile marketing’ culture based on project management \npractices originating in software development that have been growing in popularity among \npractitioners. The thesis identifies a number of ‘classes’ of marketer identities: managerially \nsupplied ; technologically afforded ; socially afforded ; emergent ; and, performed along with what \neach type enables one to do. Using ideas from Figured Worlds theory and multimodal discourse \nanalysis, a heuristic framework is then developed made of the elements ‘ matter ’ (phenomena), \n‘ meaning ’, mediators , ‘ me ’ (identity) and ‘ motion ’ (action) to study how these identities are \nused 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’. \nFinally the ideas developed through the first three phases of the thesis are applied in a final phase \nin which Canango begins using a new digital collaborative work platform. The study looks at the \nidentity implications of this move, evidencing the ways in which the work platform serves as a \n‘bridge’ between worlds and how such bridges may be used to change worlds and make new \nones.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 teacher head, 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".