Situated Design and False Creek Futures
Bibliographic record
Abstract
Extractivist data collection through citizen science initiatives produces aggregate data that can provide useful insights for policy advocacy. However, these extractivist information systems do little to nurture locally engaged and creative eco-social geographies, support decolonization, or address crises of imagination. In response, this piece asks: What types of interactive information systems would support emplaced processes of relationality, engagement and creativity in urban shoreline ecosystems? How can design support interactive eco-social geographies as terrains for thinking, connecting and enacting? We address these questions through deeply situated work in an urban marine ecosystem called False Creek located in Vancouver, Canada. False Creek is a site of ecological remediation from industrialization and urbanization, and as part of Canada's colonial occupation of unceded first nations territories, it is also a significant site of settler-indigenous reconciliation. This work delves into local history and contemporary relations to orient the production of community engaged design practices and artifacts that prioritize relationality, engagement and creativity. We critically assess our work to design appropriate information systems against the complex goals of enabling interactive, emplaced, enmeshed and reconciliatory eco-social systems.
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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.029 |
| Scholarly communication | 0.021 | 0.007 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".