Smartphones—The good, the bad and the ugly consequences of use
Bibliographic record
Abstract
Smartphones-The good, the bad and the ugly consequences of useDuring the last five decades, nothing has reformed our way of life as much as the Internet.Those of us over the age of 40 can remember using wired telephones, how to hand write letters, find our way, play in nature, use cash and many more practical skills from the time before the emergence of Internet-connected mobile devices such as smartphones and tablets.Admittedly, these devices do make life easier and safer in many ways.One example is the recent creation of the 112-emergency application (app), which localises your geographical position and calls assistance in case you get lost or injured.As a parent, a smartphone can help you stay in close contact with your children-even if they do not always agree on the benefits-more than ever previously possible.Many innovators also have great hopes that smartphones will help improve lifestyle habits through apps that encourage physical activity and other health-related behaviours.However, so far, there is only modest evidence that apps can achieve and sustain these behavioural changes, and for children there is less research to support this prospect.1
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".