Critical media literacy in Canadian classrooms: The re-education of media savvy children
Bibliographic record
Abstract
Over the latter half of the 20th century, a number of technological innovations brought about a major shift in the Canadian media environment whereby we have seen traditional media, such as newspapers and radio, eclipsed by ubiquitous, state-of-the-art technologies that are incredibly vivid and burgeoning with interactive potential. New media have appeared while older media have evolved to offer us hundreds of channels and virtually unlimited access to intonation and entertainment. Along with these developments, our acceptance and appetite for media and technology has also shifted. Cable and satellite subscriptions, Internet access, and mobile telephone use have also increased substantially in the last decade. Given what appears to be a vigorous proliferation of media technology, it is hardly surprising that children are becoming remarkably 'media savvy'. Many children today can program a VCR or a mobile phone; they can navigate the Web while "chatting" on-line with friends across the globe; and they can manipulate the most advanced video and computer games, which can be an awkward task for most adults. But trying to keep pace with technology can be a difficult and expensive challenge, particularly as the competitive market for technological goods renders equipment obsolete ever-more rapidly, year by year.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.023 | 0.007 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".