Bibliographic record
Abstract
Designed Landscapes is a case-by-case study of 37 significant, existing works of landscape design worldwide, largely constructed since the Renaissance. Being an informative and easy-to-read reference volume for practitioners and students alike, it presents key precedents in landscape architecture using site plans and recent photographs to showcase each project. Organised and presented in 12 sections based on project type, each project is examined based on date, previous site condition, designer(s), design intentions, current composition, unique features, ownership and management, and comparable projects. Each chapter offers an insightful critique of the featured projects. Written by the authors of Great City Parks, the book posits that these carefully selected key projects have maintained their status throughout the ages because they express values and design intentions that continue to inform the practice of the landscape architecture in the present day. The book concludes with a ten-point summary of lessons for professional practice gleaned from the studies. Including a wide range of case studies from countries including many in western Europe, the United States, Canada, India, Japan and China, and lavishly illustrated with over 200 full-colour images, the book is a must-have volume for anyone interested in the history and current practice of landscape architecture.
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 | 0.001 | 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 | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.047 | 0.008 |
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".