Bibliographic record
Abstract
Silicon valley billionaires, particularly figures like Elon Musk and Peter Thiel, have ascended beyond their roles as entrepreneurs to become political actors and architects of new power structures. This paper explores how the cultural mythologies of new money—marked by individualism, conquest, and a rejection of democratic constraints—frame these figures as protagonists in self-mythologized hero’s journeys. Using Roland Barthes’ theory of ideological myth and Erving Goffman’s dramaturgical model of self-presentation, the paper examines how billionaires cultivate influence through media spectacle, personal branding, and narrative control. Through the cases of Musk, Thiel, and former president Donald Trump, it traces how silicon valley’s political ethos has evolved from behind-the-scenes lobbying to overt attempts at authoritarian governance. Drawing on investigative reporting and theoretical frameworks, it argues that these figures operate not as a unified political movement but as rival protagonists competing within overlapping myths. Ultimately, the paper contends that understanding these performances is crucial to resisting their consolidation of power—and that exposing the narrative is the first step to reclaiming reality.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".