Media literacy and neo‐liberal government: pedagogies of freedom and constraint
Bibliographic record
Abstract
This paper examines relations between media education discourses and teachers’ reflections on their work with students around media. Based on a reading of curriculum documents and scholarly debates about media literacy, as well as conversations with teachers in Toronto, I ask how – and whether – formal discourses, common sense and local practices are connected in teachers’ talk. My assumption is that media education forms a set of discourses that are ‘made up’ in part through statements and debates, circulating through professional and academic journals, books, curriculum documents, courses, workshops, conferences, web‐sites, electronic communication and so on. Competing claims are made to establish what counts as media education and to assert what good media pedagogy should do and be. I then ask what teachers make of such claims and how – and whether – they are influenced by them. The first part of the paper traces some features of media education discourses over the past thirty‐plus years, while the second reports on group interviews with teachers. I show that teachers do not passively adopt or adapt to notions of media education that circulate in formal discourse. Rather, they actively constitute notions of media, youth, earning and pedagogy through their practices and through their conversations about their work with students. The paper concludes with a speculation that the media education classroom may be a particularly fertile site for the production of neo‐liberal subjects.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.068 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| 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".