Bibliographic record
Abstract
We are surrounded by increasingly complex networks of smart objects, yet our understanding and attachment to them is rather limited. One way to support stronger end users’ engagement with such complex technologies is by involving them in the design process and, with the advent of Arduino prototyping platform, even in their making. While DIY practice offers the potential for stronger user engagement with physical artifacts, we know little about end users’ DIY practice of making complex electronic technologies and their potential to ensure engagement with such devices. In this article, we report on interviews with 18 participants from two green communities who built and used an open source DIY energy monitor, with the aim to explore the end users DIY practices of making such complex electronic devices. Findings indicate four key qualities of DIY monitors: transparent modularity, open-endedness, heirloom, and disruptiveness, and how they contribute to more meaningful engagement with the DIY monitors, elevating them from the status of unremarkable objects to that ofthings. We conclude with three implications for design for supporting end user development of complex electronic DIY: designing transparent open hardware technologies, standardizing communication protocols for the current and future DIY of IoT, and deliberately calling for personal investment and labor in the assembling of DIY kits.
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 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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.002 |
| 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".