Developmental and family considerations in internet use disorder taxonomy. Commentary on: How to overcome taxonomical problems in the study of Internet use disorders and what to do with “smartphone addiction”? (Montag et al., 2020)
Bibliographic record
Abstract
Montag, Wegmann, Sariyska, Demetrovics, and Brand (2019) propose an important framework surrounding the taxonomy of problematic internet usage, with particular applications to disentangling the role of mobile and other handheld devices versus stationary platforms. This is a critical contribution, as organizational frameworks have begun to move past "whether" there is disordered internet use, and towards better understanding the complex and multifaceted ways in which internet usage can be related to psychological maladjustment. In the present commentary, we encourage authors to extend this framework by incorporating developmental complexities. Montag and colleagues' (2019) contribution is discussed with reference to children and families, including: (1) the conceptualization of problematic internet usage and associated behaviors across the early years, (2) the types of internet use and devices that are most salient for young users, (3) the embedding of children's internet consumption within the context of a broader pattern of family media usage, and (4) the construct of behavioral addictions in pediatric populations. Recommendations for science and practice are briefly discussed.
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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 | 0.013 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.040 | 0.034 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".