Connecting Theory and Practice: Implications of Coherence Theory in the Fight Against Fake News
Bibliographic record
Abstract
Fake news and virally-spread misinformation online have been identified as an increasingly pressing concern, one which LIS professionals may have a role in combatting. The insidious nature of this phenomenon is such, however, that correcting wrong information after the fact is insufficient to alter previously held incorrect beliefs. This work uses the Coherence Theory of truth to frame a conceptualization of how fake news creates “truth” for people on the basis of “influential people”. Accepting this theory requires that for LIS professionals to combat this phenomenon, the myth of neutrality must be abandoned and the LIS-approved “truth” amplified. Les fausses nouvelles et la désinformation diffusée de façon virale en ligne ont été identifiées comme une préoccupation de plus en plus pressante où les professionnels de l'information peuvent avoir un rôle à jouer. Cependant, la nature insidieuse de ce phénomène est telle que la correction des informations erronées après coup ne suffit pas à modifier les croyances incorrectes précédemment transmisses. Ce travail utilise la théorie de la cohérence de la vérité pour encadrer une conceptualisation de la façon dont les fausses nouvelles créent la «vérité» pour les gens sur la base d'«influenceurs». Accepter cette théorie du rôle des professionnels de l'information dans la lutte exige l'abandon du mythe de la neutralité ainsi qu'une mise en valuer de la validation professionnelle de la «vérité».
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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 | 0.059 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.010 | 0.073 |
| Scholarly communication | 0.016 | 0.031 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".