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Record W3197432789 · doi:10.1145/3466725.3466762

Opportunities and Scaffolds for Critical Reflection on Ethical Issues in an Online After School Biowearable Workshop for Youth

2021· article· en· W3197432789 on OpenAlexaff
Alissa N. Antle, Alexandra Kitson, Yumiko Murai, John Desnoyers-Stewart, Yves Candau, Azadeh Adibi, Katrien Jacobs, Zoe Dao-Kroeker

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsReflection (computer programming)Critical reflectionSet (abstract data type)Process (computing)Computer scienceEthical issuesEngineering ethicsKnowledge managementPsychologyEngineeringPedagogy

Abstract

fetched live from OpenAlex

The rapid adoption of biowearables, such as smartwatches, raises ethical issues as youth are increasingly being tracked, monitored and given feedback on a growing number of measures. To address this pressing need, we investigated how to support youth to understand and explore these ethical issues grounded in the processes of prototyping during an afterschool online critical making workshop. The main contribution of this paper is our critical reflection framework, consisting of three interrelated components: ethical issues, technical opportunities, and reflection scaffolds. We focus on ethical issues related to the potential for biowearables to negatively impact six constructs taken from child development. We describe how we created a biowearable-tangible prototyping kit that has under-determined design decision points, creating technologically-mediated opportunities for reflection during the iterative prototyping process. Third, we present a set of critical reflection cards created to support youth to explore the ethical issues related to those decision points. We provide two scenarios from a pilot study that illustrate our framework in action, providing preliminary validation for our approach in an online environment.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.569
Threshold uncertainty score0.482

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.202
GPT teacher head0.420
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2021
Admission routes1
Has abstractyes

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