Bibliographic record
Abstract
Abstract CityStudio is an adaptable, plug and play model that helps global cities create a permanent partnership with local higher education institutions (HEIs) for collaboration, projects, and mutual benefit. Since launching, CityStudio has seen well over 906 city staff working with 16,861 students on 3,578 projects, contributing well over 300,000 student hours to local civic priorities in three countries. CityStudio assists cities to identify and distribute priority needs to local HEI’s universities, providing increased capacity for cities and work-integrated learning opportunities for students on real-world projects in areas such as sustainability, equity, livability, and social justice. While projects directly support local strategic planning goals, they also align with the United Nations Sustainable Development Goals (UNSDGs). The dream of CityStudio is that students take a seat at the table of civic power, joining and helping the city with their needs and challenges for a better planet. But we find ourselves asking, will tomorrow be worse? Worse for democracy, worse for the environment, and worse for equity and choice? In our unique facilitator and translator position between large public institutions, across a growing network, we explore daily how to meet this moment meaningfully.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.099 | 0.042 |
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".