Bibliographic record
Abstract
While public space is meant for all to participate in and use, it does not inherently mean it is accessible, suitable, or appropriate for all users. Likewise, the urban commons, a concept that posits the collective pool and ownership of resources in an urban community, does not automatically qualify all residents to have access and democratic participation for such resources. The objective of this exploration is to exemplify, understand, and synthesise the best practices contributing to the production of generative urban commons. Placemaking will be explored critically in this paper first to understand the concept at its root, the pitfalls if not followed conscientiously, and the methodological underpinnings of the approach. To illustrate practical applications to these assertions, this paper exemplifies PlaceCity: Placemaking for Sustainable, Thriving Cities , distinctly, the case study of Hersleb High School in Gronland, Oslo. The Hersleb High Schoolers are at the forefront of this case study, as the PlaceCity project sought to engage and co-create the area together with the youth to foster a collective sense of belonging, hope for the future, and enliven the public spaces.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".