Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Cities around the world have declared their intention to become “smart.” Being a smart city involves adopting widespread urban technologies that enable the collection of data from citizens to monitor and control infrastructural issues such as public utilities, transportation, communication, waste disposal, and traffic management. There are numerous advantages associated with these urban technologies, such as the ability to improve environmental sustainability, control traffic, provide mobility options, and allow for more democratic participation by citizens. There are also disadvantages associated with smart city systems. Critics argue that these technologies are central to surveillance capitalism, raising issues of privacy and data protection. Others argue that smart city strategies simply reflect the economic imperatives of platform capitalism and serve the needs of large corporations rather than citizens. In response to these critiques, many cities have moved to improve the technological sovereignty of their citizens via strategies that encourage public participation, protect privacy, and serve the needs of the public as well as corporations.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it