Bibliographic record
Abstract
“Transparency” is continually set as a core value for cities as they digitalize. Global initiatives and regulations claim that transparency will be key to making smart cities ethical. Unfortunately, how exactly to achieve a transparent city is quite opaque. Current regulations often only mandate that information be made accessible in the case of personal data collection. While such standards might encourage anonymization techniques, they do not enforce that publicly collected data be made publicly visible or an issue of public concern. This paper covers three main needs for data transparency in public space. The first, why data visibility is important, sets the stage for why transparency cannot solely be based on personal as opposed to anonymous data collection as well as what counts as making data transparent. The second concern, how to make data visible onsite, addresses the issue of how to create public space that communicates its sensing capabilities without overwhelming the public. The final section, what regulations are necessary for data visibility, argues that for a transparent public space government needs to step in to regulate contextual open data sharing, data registries, signage, and data literacy education.
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 | 0.071 | 0.181 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.011 | 0.026 |
| Scholarly communication | 0.038 | 0.054 |
| Open science | 0.004 | 0.031 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.017 | 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".