Children and parents: media use and attitudes report. Research document
Bibliographic record
Abstract
of key themes Key findings Key findingsChildren's media access and consumption Online access and useThe amount of time 8-11s and 12-15s spend online has more than doubled since 2005… The amount of time 8-11s and 12-15s spend online has more than doubled, from 4.4 hours a week in 2005 to 11.1 hours in 2015 for 8-11s and from 8 hours to 18.9 for 12-15s.In contrast, time spent watching TV has increased slightly among 8-11s (from 13.2 hours in 2005 to 14.8 hours in 2015) and has remained stable among 12-15s (from 14.7 to 15.5 hours).…and 12-15s now spend nearly three and a half hours a week more online than they do watching a TV set This overall increase in time spent online was also visible between 2014 and 2015, from an average of 12.5 hours to 13.7 hours a week among 5-15s.The increase is particularly evident among 12-15s, who now spend 18.9 hours a week online, up from 17.2 hours in 2014.This is nearly three and a half hours more than they spend watching television on a TV set (15.5 hours). More children have internet access at home than in 2005…Although the proportion of children who go online, either at home or elsewhere, has not increased since 2014, ranging from 39% of 3-4s to almost all 12-15s (98%), it has increased only slightly since 2005 for 12-15s, when 94% used the internet at home or elsewhere 1 .Access at home has increased more substantially.In 2005, 61% of 8-11s and 67% of 12-15s had access to the internet at home.In 2015 close to nine in in ten 8-11s (91%) and nearly all 12-15s (96%) have internet access at home, either through a fixed broadband connection or through using a mobile network signal.In 2005 less than two-thirds of these home connections were broadband and 21% of 8-11s and 28% of 12-15s still had dial-up. …and compared to 2005, more are going online in their bedroomIn 2005 3% of 8-11s and 13% of 12-15s had internet access in their bedroom.In 2015 this has increased to 15% of 8-11s and 34% of 12-15s who have internet access via a desktop, laptop or netbook in their bedroom, and many also use portable devices, like tablets and mobiles, to go online.One in ten 5-15s now only go online using a device other than a desktop or laptop, an increase since 2014 Children aged 5-15 are less likely in 2015 than in 2014 to use a laptop or netbook (62% vs. 66%) or a desktop computer (28% vs. 32%) to go online.In contrast, one in ten 5-15s (11%)
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.007 |
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".