Media education and the limits of “literacy”: Ecological orientations to performative platforms
Bibliographic record
Abstract
Recently, talk of “fake news” – and its relation to wider epistemic crises, from climate denialism to the creep of global ethnonationalism – has renewed attention to media literacy in education. For some, revived discussions of media literacy offer protection (e.g., strategies for identifying and critiquing media bias and misinformation). For others, they offer empowerment (e.g. equipping youth to produce media messages that challenge misinformation or represent marginalized perspectives). In this article, we consider how such approaches, while often generative, retain a focus of media pedagogy that centers the actions of individual humans – namely, “literacies”, or practices associated with the interpretation or creation of media texts.
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.012 | 0.104 |
| Scholarly communication | 0.021 | 0.016 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".