Sustainability Design: Lessons from Designing A qGreen Mapq
Bibliographic record
Abstract
The prevalence of urban agriculture groups mobilizing to create change in cities provides a rich opportunity to understand how these communities use and can design ICTs to support sustainability.In particular, organizations are using 'green maps' to make visible local projects, initiatives, and features, in order to reduce entrance barriers and increase participation.This paper reflects on the role of ICTs in these communities as well as the role of design in addressing sustainability concerns.It reports on a design project that developed a green mapping platform to ameliorate the challenges that individuals face in discovering and participating in community-based 'green' initiatives.In order to do so, the project adopted sustainability design principles and a participatory approach.While preliminary evaluation concluded the project did not achieve its original objectives, it provided a valuable exploration of practises to address and evaluate sustainability in design projects.It highlighted the value of participation in processes rather than creation of technology products and pointed to lacking support for sustainability in current methods and techniques for systems design.The paper ends with reflections on sustainability design opportunities for community mapping and identifies future areas for exploration.
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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 | 0.017 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".