Learning in public space: The design process behind Science World’s Environmental Trail
Bibliographic record
Abstract
Urban designers and landscape architects have begun to devote more of their practice to the creation of learning opportunities in public spaces. Very little research has been conducted, however, into how these public “learning environments” have been designed. This thesis focuses on a case study of Creekside Park’s TD Environmental Trail (TDET) which surrounds Vancouver, Canada’s Science World. It offers interactive exhibits and interpretative posters that explore a number of sustainability-related themes. The research here reconstructs TDET’s design process through interviews with key participants as well as content analysis of planning and design documentation such as the City of Vancouver’s development permits. The evidence compiled reveals how the TDET became a part of a larger urban design process undertaken between 1999 and 2013, negotiating the boundaries between the site’s public and private spaces. It reduced Creekside Park’s public space through creation of the gated fare-paying “Ken Spencer Science Park”, and in exchange, provided improvements to the remaining space, including pedestrian and bicycle pathways, landscaping, and the TDET. This thesis studies the original and evolving intentions behind the TDET, shining light on the multiple images, forces, actors and decisions that led to the creation of its interactive exhibits and interpretative posters. In so doing, it provides first steps in evaluating Vancouver’s public interactive space.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.028 |
| Scholarly communication | 0.019 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".