Mainstream media and national identity constructions: A commentary on Kathleen Buddle’s ‘Consolidating codes: “Technosociability” and Winnipeg’s NDN youth networks’
Bibliographic record
Abstract
Abstract Kathleen Buddle’s contribution ‘Consolidating codes: “Technosociability” and Winnipeg’s NDN youth networks’ to this special issue on minority media pursues a different and special track as it deals with the impact of the media on minorities, in this case Canada’s First Nations, and how Photovoice/film-making projects empowered marginalized youth in Manitoba. In addition, it addresses the mission of the Journal of Applied Journalism and Media Studies as it attempts to bridge the gap between theory and practice. Vivian Giang comments on Buddle’s article and sheds more light on the role of alternative media not only in preserving minority identity, but also in contributing to national mainstream media.
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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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.030 | 0.042 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.040 | 0.046 |
| Insufficient payload (model declined to judge) | 0.003 | 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".