How and why does official information become misinformation? A typology of official misinformation
Bibliographic record
Abstract
It is important to widen the understanding of misinformation in different contexts. The findings of this qualitative study showed that official information can be misinformation. Official information, which is information concerning and/or coming from official services and processes, was studied with semi-structured interviews in two contexts in which support with information was needed. Four types of misinformation were found: outdated, conflicting, and incomplete information and perceived intimidation. Official information has characteristics related to structural factors, language, and terminology, as well as encounters that make it prone to misinformation. A typology of official misinformation was created to show the nuanced nature of misinformation and the different social, contextual, and situational factors surrounding misinformation. In-person support may be needed to tackle misinformation. Official information can be made clearer and more suited to different groups, which also diminishes the risk of misinformation.
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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 | 0.011 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.008 | 0.023 |
| Scholarly communication | 0.009 | 0.020 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| 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 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".