Re-Grounding: The Practice of PUBLIC WORK
Bibliographic record
Abstract
Marc will discuss the recent work of PUBLIC WORK, the studio he co-founded based in Toronto, Canada. Toronto has served as both a laboratory and inspiration for exploring the complex processes of building a city in which the public realm is central to its quality of life. The ambiguous identity of Toronto’s cityscape at the beginning of the 21st century leaves space for the re-articulation and design of the public realm, a chance to make source landscapes more vivid in the city and re-engage the civic imagination through landscape. In this talk, Marc will explore some of the themes which drive the work of the practice and share recent projects. Marc Ryan is principal and co-founder of PUBLIC WORK, a Toronto-based office for urban design and landscape architecture focused on one of the foremost public topics today—the intelligent evolution of the contemporary city. Established in 2012, the studio aims to produce transformative works that invigorate the public realm, optimize and enhance the performance of urban and natural systems, and support public life by adding new layers of experience to the city. PUBLIC WORK has emerged as a leading design studio who are innovators of transforming under-utilized terrain into new urban landscapes. Marc is a landscape architect dedicated to the design of the public realm and life of cities. Drawing from an education in both landscape architecture and architecture, his work seeks synthesis in the rediscovery of the public realm within challenging environments amidst rapid urban growth. Throughout more than two decades of practice in Canada, the United States and Europe, he has dedicated his passion, advocacy, and professional work toward elevating the role of landscape and the public realm in shaping the quality and experience of cities. His design practice focuses on projects for the public realm using landscape as a primary medium, providing leadership to uncover and create a dramatic new sense of place. He is currently dedicated to guiding public realm design, planning and infrastructure projects at multiple scales so they relate powerfully to their context and enhance the quality and experience of urban life.
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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.044 | 0.129 |
| Scholarly communication | 0.026 | 0.015 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".