Bibliographic record
Abstract
Towns and cities are places influx — and this applies equally to their population structure and social make-up. The last few decades have seen an accelerating change in age, household and income structures. Many of the challenges posed by this shift were anticipated by the Ministry for Building, Housing and Urban Development (now the Federal Ministry for Transport, Building and Housing). For example, the „Experimental Housing and Urban Development" programme experimented with various models for responding to the ageing of the population and to the growing divergence of life-styles. In 1999 a programme was launched jointly by the federal government and the Länder with the title „Urban Quarters in need of special development — the Social City" with the aim of addressing the growing social polarisation found within towns and cities. This article discusses the actual challenges encountered in implementing this „quarter-orientated” approach. The concept of a „social city” was intended to be driven by an overarching vision and the consent of the civic society, and not simply to be the outcome of a number of piecemeal departmental measures and sectoral policies. It is seen, after all, as a matter not of compensatory but of enabling policy.
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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.023 | 0.011 |
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".