Putting media under the microscope: Understanding and challenging media's influence on the health and well-being of children and youth
Bibliographic record
Abstract
Today's parents, themselves raised on television, are now raising the Internet generation and it is clear that they are feeling blind-sided by some of the challenges of managing this new medium in the home (1). The new digital culture has come upon us all very suddenly, but unlike parents, Canadian children are virtual trailblazers in the technology revolution. Eighty per cent of Canadian children have Internet access at home and almost 50% are online for at least 1 h every day, most with no adult supervision or basic household rules regarding Internet use (2). A lot has been written about the influence of media, particularly television, on the psychosocial development and physical well being of children and youth, for good reason. Everyday, Canadian children and youth are exposed to messages from a host of media including television, movies, magazines, the Internet, video games, music and music videos, and all forms of advertising. While the media offer young people many opportunities to learn and be entertained, how young people interpret media images and messages can be a contributing factor to a number of public health concerns (3–6).
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".