Bibliographic record
Abstract
This book is one of the outcomes of the DIVERCITIES project. It focuses on the question of how to create social cohesion, social mobility and economic performance in today’s hyperdiversified cities. The project’s central hypothesis is that urban diversity is an asset; it can inspire creativity, innovation and make cities more liveable and harmonious. To ensure a more intelligent use of diversity’s potential, a re-thinking of public policies and governance models is needed. Headed by Utrecht University in the Netherlands, DIVERCITIES is a collaborative research project comprising 14 European teams. DIVERCITIES is financed by the European Commission under the 7th Framework Programme (Project No. 319970). There are 14 books in this series, one for each case study city. The cities are: Antwerp, Athens, Budapest, Copenhagen, Istanbul, Leipzig, London, Milan, Paris, Rotterdam, Tallinn, Toronto, Warsaw and Zurich. This book is concerned with Rotterdam. The texts in this book are based on a number of previously published DIVERCITIES reports.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.040 | 0.007 |
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".