Bibliographic record
Abstract
Large housing estates: for some people these three words symbolise all that is wrong in urban planning. Large is wrong, because many people prefer a living surrounding that reflects the human scale. Housing as a single function is wrong, because mixed areas are livelier. And estates are wrong, as these refer to top-down planned areas which the residents themselves have no say in. Although many such estates function well, others have proved to be in serious problems. The question is how to deal with this legacy. For these estates to recover, an integrated solution is needed. Large-scale problems require large-scale interventions. The Amsterdam Bijlmermeer area has been the most deprived and stigmatised area in the Netherlands for at least a quarter of a century, despite its glorious design in the 1960s. The Bijlmermeer can now be considered as a leading case for area regeneration. The author has followed this fascinating neighbourhood for years and provides an analysis of its construction, fall and recovery. Moreover, he compares the Bijlmermeer with many other large housing estates in the Netherlands and abroad, and analyses what conclusions may be of use for other areas.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".