Bibliographic record
Abstract
The ‘urban village’, as it has come to be known since the 1980s, has very little to do with the rural. This coupling of terms, however, evokes a longer history of attempts to marry the urban with the rural, without either losing their distinctive attributes. This article traces this genealogy to Patrick Geddes's conceptualisation of the term in India, and focusses the analysis on two projects: the 1930 ‘Green City’ proposal by the constructivists Moisei Ginzburg and Mikhail Barshch, and the planning of Dodoma, Tanzania’a post-colonial capital, by the Canadian firm Project Planning Associates and the American James Rossant in the late 1970s. Both projects addressed nascent socialist societies, but the former was part of an industrialisation campaign while the latter was part of a villagisation campaign. By re-imagining the village as a component that can be integrated into the city, both aimed at a socio-economic reconfiguration of the town-country relationship on a territorial scale. Reconsidering the term ‘urban village’ as part of a broader history of urban-rural planning emphasises how experiments in the Global South, as well as other locations outside the professional hegemonic centre, shed new light on the discipline's core assumptions about the urban-rural divide.
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 | 0.005 | 0.021 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".