Do you know the Frankenbite? Media Literacy on the power of story and editing
Bibliographic record
Abstract
Over the last decade, the hybridization of information and entertainment into infotainment in legacy and social media, plus technical innovations from Deep Fakes to AI, have escalated the challenge of upholding ethical principles in media creation. Our media literacy curriculum for High school students addresses the prevalence of fictional storytelling in fact-based media. Our goal is to guide students as media consumers to understand and explore the ways in which picture editors alter facts to favour story, and to decipher fact from fiction in audiovisual media. However, the responses and feedback we received after two trial years revealed a disturbing general mistrust of students in all media, questioning of any empirical facts and established science. This article explores this fundamental epistemic dilemma in teaching media literacy, leading to a crisis in society. We are experiencing a shift in the media landscape as dramatic as the transition from oral to written storytelling.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".