New Screens and Young People: Crossing Times and Boundaries what roles do they play in their everyday life
Bibliographic record
Abstract
New information and communications technologies have become extremely dynamic. While content in the past was more or less controlled and regulated, it is now more and more free to access, and increasingly independent of any formal institutional framework. Images on screens, which used to be viewed on different platforms in specific locations and at more predictable times, now cross through space and time, and this particularly for the younger generations. Some questions arise in this context: what is the role and what are the effects, for example, of film content and video games on young people? What role does rating systems play with respect to these young people and their parents? In order to answer such questions, we need to understand their needs, expectations and skills. Some consider young people to be passive, easy to manipulate, unaware of their values and entirely lacking in critical thinking skills. Others see them instead as active users, capable of using knowledge and competencies. Given our objectives, we chose a qualitative approach designed to take young people’s everyday environment into account in the construction of their relationships to on-screen images. Family interviews (semi-structured interview guides), logbooks and digital video cameras were used to gather information in the families. Discussion groups were held with young people and parents separately and evaluation groups with young people aged 14-16 were held in our research facilities. Answers to such questions about the effects of images were found to be complex and full of nuances, despite the fact that there are some who would want simple yes or no answers that support their views. “This invention will produce forgetfulness in the minds of those who learn to use it, because they will not practice their memory. Their trust in writing, produced by external characters which are no part of themselves, will discourage the use of their own memory within them.″ Plato, Pheadrus, 275b, 300 B.C.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".