AI and Apps in Early Childhood: The Role of the Family
Bibliographic record
Abstract
This research is part of a project that aims to map datafication and platformization in the early years of life. Analyzing the educational quality and privacy protection of apps for children was considered fundamental for the project. Children are constantly exposed to technology, especially when using digital applications (Rocha and Nunes, 2020). Some scholars have focused on the problem of data extraction and monetization, with the pervasive use of applications even in the domestic sphere (Barassi, 2020; Pangrazio and Mavoa, 2023). According to several studies, parents' digital exposure and decisions on children's digital footprint are crucial (Jibb et al., 2022; Pimienta et al., 2023; Raffaghelli, 2022). The research follows a postpositivist approach (Petter and Gallivan, 2004), which is useful for understanding phenomena through data collection, categorization, and analysis. Data were collected on 30 apps, and the applications were evaluated according to different categories using a systematic database. Data analysis was based on quantitative processing through descriptive and inferential statistics. Empirical validation was carried out using inter-rater agreement, and a reliability analysis was introduced in the results. The results highlighted some critical issues regarding the use of technological consumption within families and a disparity between the evaluations of the apps carried out by the general population and those carried out by expert educators. The research highlights the need for parents to be more vigilant when choosing apps for their children, considering learning value, clarity of information, security and ethical use of data.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".