Bibliographic record
Abstract
Abstract Media ecologists Marshall McLuhan and Neil Postman considered education to be the essential way to counter the negative effects of technopoly, which define a culture that deifies technology, and seeks support, authority and its satisfactions from it. The relatively new subject of media literacy seeks to convey awareness of media’s potential harms and to shield its users from becoming unwilling servants of technology. Marshall McLuhan wrote extensively about education and created the very first high school media studies curriculum for the National Association of Educational Broadcasters (NAEB) in the United States, influencing the media literacy practitioners to come. The first generation of media literacy teachers in Canada and the United States adopted a broad conception of what was needed for students to be considered media literate. The question to be considered is whether, since then the teaching of media literacy has become focused more narrowly on content analysis and the how-to aspects of media use, while neglecting the theoretical and conceptual roots of media studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.009 | 0.019 |
| Insufficient payload (model declined to judge) | 0.010 | 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".