CIVIC DISCOURSE AND SPACE ACTIVATION AS A COLLABORATIVE CITY-BUILDING PROCESS
Bibliographic record
Abstract
With tactical urbanism, we utilize a method that involves citizens by taking a hands-on approach to the city-building process.The undertaken, short-term transformation through co-created projects builds on existing individual and collective identities.Applying tactical urbanism to the public realm will shift current perceptions of a space from a service to a collaborative venture that enables engagement for all persons.Together, a NEXT Calgary is created.The outreach for this project focuses on the public and civic discourse, specifically addressing the conversations about the built environmental in Calgary, AB.This paper will provide examples on how tactical urbanism was implemented across Calgary whether through little surprises woven into the urban fabric or how the project allowed Calgarians to (re)connect with the everyday.There are two angles to this approach.One is the public perspective and how the public related to the work, and the other angle is the official perspective of how bodies and the city have grudgingly come along the journey.
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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.045 |
| Scholarly communication | 0.021 | 0.014 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".