Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it