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Record W2530197978 · doi:10.2991/ict4s-16.2016.20

Sustainability Design: Lessons from Designing A qGreen Mapq

2016· article· en· W2530197978 on OpenAlexafffund
Dawn Walker, Christoph Becker

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicGeographic Information Systems Studies
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaVienna Science and Technology Fund
KeywordsSustainabilityComputer scienceArchitectural engineeringEngineering

Abstract

fetched live from OpenAlex

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.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.967
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.065
GPT teacher head0.344
Teacher spread0.279 · 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

Citations5
Published2016
Admission routes2
Has abstractyes

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