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
Purpose The purpose of this paper is to illustrate that the current efforts to combat the epidemic of fake news – compiling lists of fake news sites, flagging stories as having been disputed as “fake,” downloading plug-ins to detect fake news – show a fundamental misunderstanding of the issue. Design/methodology/approach This paper explores the plummeting believability ratings in conventional news outlets, as well as current efforts to combat fake news. These concepts are situated in the post-truth era, in which news is upsold on the notion of belief and opinion. Findings This paper finds that, in combination with a general mistrust of all news, a fundamental flaw in the system of clicks-as-reward allows fake news and other clickbait to gain unobstructed virality. Originality/value Fake news is a widely discussed topic right now. As this is primarily an issue of information literacy, library and information professionals need to understand, discuss, and address this issue as one that is directly related to the profession.
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 | 0.000 | 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 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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