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
In the past US election cycle, and mirrored by similar events in Europe, two trends have come to dominate social discourse: truthiness (the validity of something based on how it feels) and post-fact (taking a position that ignores facts). Human discourse has always contained elements of these, but the nature of the Internet and social media has pushed truthiness and post-facts to new levels. The purpose of this paper is to explore the complicated relationship brands have with fake news and discuss the implications for brand management of a post-truth world. We explore the complicated relationship brands have with fake news: Brands both fuel fake news and are burned by it. Next, we turn to the intellectual and instrumental roots of the post-truth world: postmodernism and its technological enablers, show how marketing became a purveyor of the postmodern worldview, and how brands have increasingly adopted truthiness and post-fact positions. We offer managers a way out of the postmodern cul-de-sac, discussing ways brands can be rethought and managed in a post-rational world.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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