How Truthiness, Fake News and Post-Fact Endanger Brands and What to Do About It
Bibliographic record
Abstract
Abstract Brands can interact both directly and indirectly with fake news. In some instances, brands are the victims of fake news and, other times, the purveyors. Brands can either finance fake news or be the targets of it. Indirectly, they can be linked via image transfer, where either fake news contaminates brands, or brands validate fake news. To control the risk of negative image transfer, the authors propose technical actions to address false news and systemic steps to rethink the management of brands in order to inoculate against various forms of “fakery” and to reestablish stakeholder trust. Systemic solutions involve a rethinking of brands and branding. Too often, brands have become uncoupled from the reality of the offerings they adorn. But brands are not ends in themselves, they are the result of outstanding offerings. They can act as interpretive frames, but they don’t unilaterally create reality, as many seem to believe. Brands should not be seen and managed as objects but as perceptual processes.
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.
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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".