Teenagers and the Data Economy: Understanding Their Dreams, Desires and Anxieties with Metaphor Workbooks
Bibliographic record
Abstract
Teenagers are in a unique position, having known no other reality than the current exploitative model of the data economy, and are particularly at risk of harm from it. Using a classroom intervention with 31 Grade 9 students, we deployed co-created Metaphor Workbooks as a tool to foster critical and reflexive thinking about their phones and data. Our research advances the HCI community's understanding of teenagers’ entanglements with the data economy, by highlighting how they experience it through their critical, reflective, and creative responses. This alludes to ways in which future initiatives could better support teenagers in developing a critical relationship with data. We identify key gaps in their understanding of the data economy and emphasize the need for critical data literacy interventions to address their limited understanding, complex emotional relationships with their phones, and the pervasive influence of technology addiction narratives.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.009 | 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".