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Record W4413924343 · doi:10.18357/otessaj.2024.4.3.88

Redesigning Computing for Openness

2025· article· en· W4413924343 on OpenAlexaffvenue
Brian Sutherland

Bibliographic record

VenueThe Open/Technology in Education Society and Scholarship Association Journal · 2025
Typearticle
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsOpenness to experienceComputer scienceBusinessPsychology

Abstract

fetched live from OpenAlex

Consumer electronics design is an easily relatable and fast-cycling field of interest to students of all kinds, but particularly to those studying information science. Within this field, the e-waste problem is a significant ethics issue. Why does the logic of using computers involve the repeated purchasing and consumption of new machines, or “molded plastic epics” (Gabrys, 2011), and their significant manufacturing expenditure of carbon? Thinking back on calculating devices which supported problem-solving and this ethical problem of repeated consumption, the simple solar calculator stands out for its durability. The reason may be the initial sustainability design: early calculators, like more recent Citizen Eco-Drive watches, use a solar ambient-energy harvesting strategy that doesn’t store electricity in batteries; consequently they are very long lasting and low maintenance. As there are very few energy-harvesting electronics devices in the market reflecting emerging green narratives of degrowth, reuse, and upcycling, this research paper reviews the history and design of some of these rare devices while discussing their energy experience design strategies in the context of modern consumer electronics. I also present a series of speculative prototypes which feature broad affordability, openness, and a more ethical consumption ethos as discussion artefacts for design education students engaging with this problem.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.364
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0020.000
Research integrity0.0000.001
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 head0.024
GPT teacher head0.363
Teacher spread0.338 · 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.

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

Citations0
Published2025
Admission routes2
Has abstractyes

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