HCI, politics and the city: Engaging with urban grassroots movements for reflection and action
Bibliographic record
Abstract
Grassroots initiatives enable communities of stakeholders to transform urban landscapes and impact broader political and cultural trajectories. In this twoday workshop, we present opportunities to engage HCI research with activist communities in Vancouver, the city hosting CHI’11. Working directly with local activist organizations, we explore the processes, materials, challenges, and goals of grassroots communities. Our bottom-up approach, including explorations of urban spaces and activist headquarters, participatory design sessions, reflection, critique and creative design of political artifacts will bring together a diverse group of HCI researchers, activists and artists. The workshop will result in concrete strategies for bottom-up activism and serve to inform the design of future interactive systems in the domain of political computing.
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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 | 0.015 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.022 | 0.038 |
| Scholarly communication | 0.029 | 0.008 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".