“God Only Knows What It’s Doing to Our Children’s Brains”: A Closer Look at Internet Addiction Discourse
Bibliographic record
Abstract
This article examines the current discourse of “ethical technology” or “tech humanism” as it relates to young people’s use of mobile and social media. Reminiscent of earlier moral and media panics surrounding the use of communication technologies by young people, the current rhetoric focuses on “internet addiction” and other health aspects, and whether and how tech companies should be responsible for the use of their products and services. It is a contested debate that has brought together reformed Silicon Valley tech entrepreneurs, policy-makers, health specialists, academics, educators, and parents. In this article we demonstrate the range of stakeholders deeply engaged in these debates to argue that while there is genuine concern about the power and influence of social media and digital technologies, fears about young people’s relationships with digital technology has been profitable, and discourse on “internet addiction” has worked in ways that protect corporations and redirect condemnation away from them and toward the young people they are claiming to protect. In making this argument, we trace a history of “internet addiction” research in order to situate the current discourse, examine the rhetorical shift that emphasizes the health effects of technology on young people, survey the stakeholders leading these debates, and assesses the corporate responsibility of tech companies that depend on the commodification of young people’s content for their bottom line.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.015 | 0.047 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.005 | 0.009 |
| 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".