The Impact of Television on Children’s Leisure
Bibliographic record
Abstract
It is highly unusual to find anything resembling a “natural experiment” where researchers are provided with an opportunity to observe the effects of factors held to be influential but impossible to isolate. One such factor is television. All know it is supposed to have effects on children’s behavior (Gunter & McAleer, 1990), but it is very difficult to measure these (Gaunt- lett, 1995; Hodge & Tripp, 1986), especially in natural rather than artifi- cially contrived circumstances. A number of studies including Brown et al. (1974) in Scotland, Murray & Kippax (1978) in Australia, Williams & Handford (1986) in Canada, and Mutz et al. (1993) in South Africa, have attempted this by studying the impact of television in communities with different levels of exposure or by monitoring responses to its introduc- tion. One of the earliest and most influential studies was undertaken in the UK by Himmelweit et al. (1958), who compared children who were viewers with controls who were not, making use of a survey of children’s leisure habits by means of diaries kept by children aged 10-11 years and 13-14 years. They also undertook a rare “before and after” study of the in- troduction of television to Norwich. The research reported here is a some- what similar study that took place over the period 1994-1998 of the ef- fects of the introduction of broadcast television in St. Helena on children aged 9-12 years.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".