Marketing (as) Rhetoric: paradigms, provocations, and perspectives
Bibliographic record
Abstract
In this collection of short, invited essays on the topic of marketing (as) rhetoric we deal with a variety of issues that demonstrate the centrality of rhetoric and rhetorical considerations to the pursuit of marketing scholarship, research and practice. Stephen Brown examines the enduring rhetorical power of the 4Ps; Chris Hackley argues for the critical power of rhetorical orientations in marketing scholarship but cautions us on the need to work harder in conceptually connecting rhetorical theory and modern marketing frameworks; Shelby Hunt explains how rhetorical processes are incorporated in his inductive realist model of theory generation, using one of his most successful publications as an illustration; Charles Marsh demonstrates what Isocrates’ broad rhetorical project has to teach us about the importance of reputation cultivation in modern marketing; Nicholas O’Shaughnessy uses an analysis of Trump’s discourse to argue that political marketing as it is currently conceived is ill-equipped to engage effectively with the rhetorical force of Trump’s ‘unmarketing’; Barbara Phillips uses Vygotsky’s work on imagination to investigate the important of pleasure and play in advertising rhetoric; and finally, David Tonks, who in many ways started it all, reiterates the need for marketers to recognise the strength of the relationship between marketing and persuasion.
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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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.009 | 0.042 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".