The Stranger Danger: Exploring Surveillance, Autonomy, and Privacy in Children’s Use of Social Media
Bibliographic record
Abstract
In this paper, we argue that censorware is one of the bogeymen that instills fear in parents whose children have access to the Internet. It is a fear that has the potential to restrict children’s autonomy and opportunities for engagement in social media. Fear regarding children’s online activities is one of the issues surrounding children’s Internet safety that does not appear to be situated in any particular social or cultural context. Among the most popular means of monitoring children online, censorware may prove even more harmful to children’s socioemotional wellbeing and development than any other form of monitoring (Boyd & Jenkins, 2006; Cloke & Jones, 2005; Helwig, 2006; Kamii, 1991; Laufer & Wolfe, 1977; Marx & Steeves, 2010; Pettit & Laird, 2002; Rooney, 2010). Inherent in the design and use of censorware are structures that inhibit children’s online and offline social interactions, their ability to develop fully as social actors, and their experience of being empowered to make informed and critical decisions about their lives, including choices relating to privacy. As well, reliance on surveillance-based approach-es to monitoring online activities of chil-dren (aged 5-14) may actually be leading to a greater danger: a decrease in oppor-tunities for children to have experiences that help them develop autonomy and independence. Our inquiry is located within a growing body of research that addresses the social implications of restricting, surveilling and controlling young children’s online activities versus nurturing individual autonomy through parental mentoring and critically reflec-tive software and social technology use.
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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".