Sociotechnical governance of misinformation: An Annual Review of Information Science and Technology (ARIST) paper
Bibliographic record
Abstract
Abstract Misinformation is a complex and urgent sociotechnical problem that requires meaningful governance, in addition to technical efforts aimed at detection or classification and intervention or literacy efforts aimed at promoting awareness and identification. This review draws on interdisciplinary literature—spanning information science, computer science, management, law, political science, public policy, journalism, communications, psychology, and sociology—to deliver an adaptable, descriptive governance model synthesized from past scholarship on the governance of misinformation. Crossing disciplines and contexts of study and cases, we characterize: the complexity and impact of misinformation as a governance challenge, what has been managed and governed relative to misinformation, the institutional structure of different governance parameters, and empirically identified sources of success and failure in different governance models. Our approach to support this review is based on systematic, structured literature review methods to synthesize and compare insights drawn from conceptual, qualitative, and quantitative empirical works published in or translated into English from 1991 to the present. This review contributes a model for misinformation governance research, an agenda for future research, and recommendations for contextually‐responsive and holistic governance.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".