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Record W1447902407

Violence on canadian television networks.

2004· article· en· W1447902407 on OpenAlexaffabout
Guy Paquette

Bibliographic record

VenuePubMed · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicMedia Studies and Communication
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsHumanitiesPublicsPolitical scienceEthnologyArtSociology
DOInot available

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.950
Threshold uncertainty score0.743

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.029
GPT teacher head0.255
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2004
Admission routes2
Has abstractyes

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