Bibliographic record
Abstract
The emergence of the Garden City movement, inspired by Ebenezer Howard’s book To-morrow: a Peaceful Path to Real Reform (1898), subsequently published as Garden Cities of To-Morrow (1902), would have an enormous impact on future urban development and town- planning worldwide (e.g., Parsons and Schuyler 2002, 78; Ward 1992; Cooke 1978). Lewis Mumford claimed that the two most important inventions of the early twentieth century were the airplane and the Garden City (Mumford 1960). The Garden City model in many ways represents the antithesis to the historic city, as a model derived from smaller rural communities with a defined size, low densities, and a wealth of green space. Many subsequent urban models have expanded upon, altered, and diverged from Howard’s ideas. The Gar- den City has radically challenged the expectation that a city is a dense, vibrant, and largely hard-landscaped environment. In fact, urban environments developed over the last half-century have in many cases been dispersed, low-intensity, and soft-landscaped en- vironments, resulting in substantial changes to the way cities are constructed, managed, and inhabited.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.035 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".