Content marketing as a propaganda vehicle for a romantic-managerial conception of artificial intelligence
Bibliographic record
Abstract
Drawing on Deloitte’s content marketing between 2017 and 2022, this study examines how a conception of artificial intelligence (AI) is shaped and disseminated according to the precepts of technical propaganda outlined by Jacques Ellul. The firm promotes a romantic-managerial conception of AI that suggests business specialists can help organizations reap the benefits of the miraculous promises of a human-centered AI, without incurring any of the drawbacks, based on the assumption that it is possible to control AI. Three complementary discursive archetypes are mobilized by the firm to spur organizations into action: the prophet, arousing enthusiasm by spreading the “Good News” about AI; the demystifier, numbing concerns about the potential drifts of the technical society; and the bird of ill omen, stoking a fear of inertia. This study highlights the relevance of Jacques Ellul’s work to stimulate the ongoing debate within the critical accounting community about the perils of AI’s proliferation in organizations and society. Beyond its traditional function of legitimizing expertise, content marketing is mobilized as a propaganda vehicle for a conception of AI seemingly shaped in the mold of the firm’s own professional services offering. Finally, by showing how the precepts of propaganda are incorporated into content marketing, this study highlights the commercialization of contemporary social issues as a pivotal step in the colonization of professional accounting services by marketing expertise.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.007 | 0.045 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".