Catalyzing Revitalization through Tactical Urbanism: Developing a Parklet Framework for The City of Calgary
Bibliographic record
Abstract
Parklets are small-scale urban activations that re-consider on-street parking as public pedestrian spaces. This relatively new urban space typology – a component of tactical urbanism – approaches public space improvement through temporary and small-scale tactics that lead to long term changes. Many cities like Calgary employed parklet-like activations in the form of outdoor patios on downtown streets during the Covid-19 global pandemic to afford citizens safe outdoor environments to gather and socialize. The City of Calgary has found success with the Outdoor Patio Program, which helps businesses extend their service footprint by utilizing sidewalks and on-street parking. While Calgary does not have a public parklet program yet, this is evidence that The City has a strong foundation for developing one. This thesis aims to fill in the gaps of a formal parklet program by conducting the initial research for starting a program. Recommendations are achieved through a tactical urbanism approach, which combines tactics – piloting a mobile parklet – and strategies – examining precedent guidelines and standards – to achieve a holistic approach in developing a parklet program. This research will recommend potential next steps for implementing a parklet program for The City of Calgary as well as a draft Parklet Handbook with suggested content and standards. This research will also provide focal areas for installing parklets in Greater Downtown Calgary and examine a new model of parklets which provides insights on an innovative approach to building, installing, and moving the parklet.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.015 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".