An AI-Assisted Topic Model of the Media Literacy Research Literature
Bibliographic record
Abstract
Media literacy, a vital field of research and educational practice, is attracting considerablescholarly attention, resulting in a burgeoning research literature. While numerous bibliometricstudies have sought to capture the key features and themes of this body of literature, its rapidproliferation requires greater scalability and stronger capability to identify and characterize latenttopics. In this study we address this gap by offering a computational bibliometric analysis ofa corpus of 4,082 research documents on media literacy, spanning the period from 1985 to2024. Through analysis of the documents’ metadata with natural language processing (NLP)using Latent Dirichlet Allocation (LDA) with Orange3, an open-access data mining softwaretool, we identify seven principal topics, each represented by a specific set of documents. Thetopics pertain to media publications and online content, critical thinking, youth behaviour,new media skills in education, news and misinformation, health (particularly among females),and communication strategies. We characterize these media literacy research topics with theassistance of a Large Language Model to generate a short synthetic description based on eachtopic’s top keywords. We complement our analysis with VOSviewer to produce co-citation mapsof publication sources and authors to identify the disciplinary structure of the field, key MLauthors, and their research contributions, which focus especially on media literacy education,digital media, behavioural issues, health impacts, and public perceptions.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".