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
Post-truth relates to the combination of tactics of influence and opinion manipulation orchestrated by powerful economic and political interests, principally targeting initiatives or ideas with a transformative potential. Post-truth strategies express themselves in multiple tactics, which happen synchronously at varied levels and through different channels. Scientifically valid information is forced to compete with narratives which are designed to create doubt or skepticism. Disinformation weakens efforts to implement policies intended to support transformative goals. The distortion, discrediting, or ignoring of scientific evidence has become a threat to our societies. This article starts by defining the post-truth phenomenon, first discussing the roots, tactics, and contextual conditions supporting its expansion. Then it explores what stance evaluators can adopt to work in this new era where people are polarized and disinformation is widespread. This article aims to raise awareness of this disruptive phenomenon and brings evaluators together to consider promising practices.
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 | 0.000 |
| 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