<i>Secret Lives of Data Publics</i>
Bibliographic record
Abstract
Conventional smart city design processes tend to focus on instrumental planning for city systems or novel services for humans. Interacting with data produced by the new services and restructured systems entailed by these processes is commonly done via interfaces like civic dashboards, leading to a critique that data-driven urbanism is bound by the rules and constraints of dashboard design [1]. Informed citizens are expected to engage with new urban information flows through the logic of dashboard interfaces. What datastreams are left off the dashboard of engaged urban experience? What design opportunities arise when dashboard visualizations are moved into the domain of mixed reality? In this two-day workshop, participants will construct prototype mixed reality interfaces for engaging the informational layer of the built urban environment. Using the Unity game engine and the Microsoft HoloLens, participants will focus on generative design in the space of data-driven interfaces, addressing issues of data access, civic agency, and privacy in the context of smart cities. Specific attention will be paid to interfaces that facilitate harmonious co-existence between humans and non-human systems (AI, IoT, etc.).
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.016 | 0.026 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.024 | 0.007 |
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".