Misinformed about misinformation: On the polarizing discourse on misinformation and its consequences for the field
Bibliographic record
Abstract
The field of misinformation is facing several challenges, from attacks on academic freedom to polarizing discourse about the nature and extent of the problem for elections and digital wellbeing. However, we see this as an inflection point and an opportunity to chart a more informed and contextual research practice. To foster credible research and informed public policy, we argue that research on misinformation should be locally focused, self-reflexive, and interdisciplinary, addressing critical questions about what counts as misinformation and why it does, the vulnerabilities of specific communities, and the sociotechnical and sociopolitical conditions that shape information interpretation. By concentrating on when and how misinformation affects society, instead of whether, the field can provide more precise insights and contribute to productive discussions.
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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 | 0.055 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.010 | 0.070 |
| Scholarly communication | 0.020 | 0.023 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".