Bibliographic record
Abstract
Since the rise of the digital age, media education policies in Canada have been developed by education ministries. This chapter suggests a framework for expanding the policy discursive space that is restricted today to policymakers by creating a policy community that meaningfully engages diverse actors in the co-construction of knowledge to inform media education policy research and advocacy agendas. Drawing on political theory, communication activist scholarship, and critical research methods, a deliberative approach is detailed in the chapter as a methodological framework for rethinking media education policy research and advocacy. After briefly discussing the rise of the deliberative approach in policy research and its application in the field of communication studies in Canada, this chapter discusses how the deliberative approach can be used as a methodology for facilitating engaged, participatory, and action-oriented research that seeks to inform media education policies and practices. In conclusion, the chapter reflects on the factors and challenges that arise in using a deliberative approach in media education policy research to advance public policy solutions.
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 | 0.137 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.029 | 0.125 |
| Scholarly communication | 0.049 | 0.038 |
| Open science | 0.007 | 0.021 |
| Research integrity | 0.013 | 0.019 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".