The role of negotiated developer obligations in financing large public infrastructure after the economic crisis in the Netherlands
Bibliographic record
Abstract
The economic crisis that started in 2009 has negatively impacted in the Netherlands the available financial resources for urban development. Dutch municipalities struggle since then with falling local financial sources, especially since active public land policy, traditionally an important additional financial source, became not so profitable anymore. One supposed effect is the limited degree to which municipalities can nowadays finance public infrastructure that serves wider areas, thus more than one specific development site (i.e. ‘large’ public infrastructure). Until now, however, there are no data available that support this claim. In this paper, we explore this and the role that developer obligations can play as an alternative, compensating financial source. Developer obligations are in many countries a growing popular public value capturing instrument, but in the Netherlands, a relative new phenomenon. On the basis of surveys, interviews and policy analysis, we conclude that at least a quarter of Dutch municipalities use developer obligations to obtain financial sources for large infrastructure. This seems, however, so far not to compensate for the diminishing of other municipal financial sources. The paper ends with some speculation about the future evolvement of developer obligations in the Netherlands.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".