New Information Literacy Model for Identifying Mis/Disinformation Falls Short of Determining and Addressing a Need
Bibliographic record
Abstract
A Review of: John, K., & Tater, B. (2025). Reframing the information literacy framework to identify misinformation and disinformation. Serials Librarian, 86(1/2), 29–55. https://doi.org/10.1080/0361526X.2025.2459765 Objective – To determine the information literacy skills needed for identifying misinformation and disinformation, examine current information literacy models’ incorporation of those skills, and propose a new information literacy model to address those skills. Design – Analysis of published literature. Setting – Publications on misinformation and disinformation and information literacy. Subjects – Information literacy models. Methods – Google Scholar was used to locate 1,378 peer-reviewed articles addressing topics related to the current study. Of these, 175 papers were selected for analysis and categorized into the following areas: misinformation and disinformation, causes of misinformation and disinformation, types of misinformation and disinformation, identification of misinformation and disinformation, library and information services, information literacy, misinformation and disinformation and information literacy, information literacy models and misinformation. Content from the studies was synthesized into a discussion and used to create a new information literacy model to address misinformation and disinformation. Main Results – The authors assert that misinformation and disinformation pose a substantial problem and that current information literacy models do not adequately underscore elements that lead to the identification of misinformation and disinformation. They point to plagiarism and poor research design as evidence that existing models are unable to assist in substantiating information. Recommendations for an information literacy model include promoting thorough analysis, emphasizing accuracy, educating users about determining the purpose of information, and integrating information and communication technology skills. Additionally, the authors propose an information literacy model that lists components of information literacy, information literacy skills, and elements of misinformation and disinformation. Conclusion – The authors suggest that their review of relevant literature shows that existing information literacy models do not facilitate the identification of misinformation and disinformation. Furthermore, the authors believe that this weakness, coupled with changes to the online information environment, necessitates an information literacy model to assist users in identifying misinformation and disinformation. Their proposed information literacy model includes elements that they believe support this need.
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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 | 0.023 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.007 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.012 | 0.027 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".