Bibliographic record
Abstract
INTRODUCTION: Over the past twenty years, the question of the effects of violence on television has figured prominently in public opinion and hundreds of studies have been devoted to this subject. Many researchers have determined that violence has a negative impact on behavior. The public, broadcasters and political figures all support the idea of reducing the total amount of violence on television - in particular in shows for children. A thousand programs aired between 1993 and 2001 on major non-specialty television networks in Canada were analyzed: TVA, TQS, as well as CTV and Global, private French and English networks, as well as the English CBC Radio and French Radio-Canada for the public networks. METHOD: The methodology consists of a classic analysis of content where an act of violence constitutes a unit of analysis. RESULTS: The data collected revealed that the amount of violence has increased regularly since 1993 despite the stated willingness on the part of broadcasters to produce programs with less violence. The total number of violent acts, as well as the number of violent acts per hour, is increasing. Private networks deliver three times more violence than public networks. Researchers have also noted that a high proportion of violence occurs in programs airing before 21:00 hours, thereby exposing a large number of children to this violence. CONCLUSION: Psychological violence is taking on a more significant role in Canadian Television.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.017 | 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".