Bibliographic record
Abstract
Most of the current academic literature on open data looks outward at the data's reuse by the public. This article describes, rather, the cultural practice of open data inside city governments. Hand-in-hand with the launch of open data policies, city governments have embraced data analytics to track performance, set goals, justify budget expenditures, direct public services, and represent their work to the public. Through an increased need to data-fy, or to transform records or actions into digital data, staff considers the analytical possibilities of existing administrative records both as economic evidence of government activities and as reusable assets with statistical and machine-actionable functions. These data practices provide a legitimized way for municipal governments to know and govern the city and manage its resources. Contended as performative acts, local governments' data practices help the city perform aspects of its functions and values such accountability, transparency, and democracy.
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 | 0.064 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.019 | 0.112 |
| Scholarly communication | 0.041 | 0.027 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".