“There are a LOT of moral issues with biowearables” ... Teaching Design Ethics through a Critical Making Biowearable Workshop
Bibliographic record
Abstract
There has been an increasing focus on teaching youth about design ethics as part of technical literacy. Biowearables are an emerging technology in which devices worn on children's bodies are used to track, monitor and provide feedback about their biological processes. In this paper we describe an online critical making workshop designed to enable students in middle school years to develop technical literacy skills that include reflection on issues related to design ethics. We investigated if and how our workshop enabled eleven youth, aged 12-14, to reflect through processes of making their own biowearable, on potential negative impacts of biowearables on their developing senses of identity, agency, autonomy and authenticity. The workshop elements included facilitated activities using custom created biowearable-tangible kit and ethics cards. Through qualitative coding and thematic analysis of moments of reflection captured with video, chat, and design journals we gathered evidence of the feasibility of promoting critical making as a means to cultivate technical literacy in youth. Our findings suggest the potential of teaching design ethics through critical making workshops and reveal a range of ways that reflection on ethical issues can be supported during making. We interpret our empirical evidence to further explore how workshop elements supported, or failed to support, learning outcomes and generalize our interpretations to propose preliminary guidance about workshop mechanisms that might be used to support ethical reflection during making.
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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 | 0.018 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".