“I’ll use it differently now”: using dual-systems theory to explore youth engagement with networked technologies
Bibliographic record
Abstract
OBJECTIVES: Many young Canadians experience high levels of networked connectivity, which some suggest may negatively impact their health. Adult monitoring has not been shown to be an effective long-term strategy for supporting young people in healthy engagement with tech. In this study, we explore the benefits of empowering young people to set healthy goals and monitor themselves. We engage with Shapka's (2019) critique of dual-systems theory, and consider the relationship between the neurological and behavioural systems in relation to adolescent internet use. METHODS: Using a youth participatory action research approach, we co-designed a project with six adolescents to explore the ways that their use of networked technologies was affecting their lives by disconnecting and observing how the lack of networked connectivity changed their experiences. The youth used a media diary to track their use of devices both before and after disconnecting. RESULTS: The main benefit of disconnecting appeared to be having the opportunity to reflect on one's own use of networked devices. This enabled the participants to reconnect in a more intentional way. Findings support Shapka's speculation that dual-systems theory, with a focus on regulation, may not be the most useful way of supporting adolescents in developing healthy habits around their wired tech. CONCLUSION: Adolescent experiences of networked technologies are complex, yet they are able to navigate this landscape with intelligent strategies. Their self-directed exploration of disconnection helped them to become reflexive practitioners who were able to revisit their use of networked technologies with new insights and self-control.
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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 | 0.011 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".