Regulation by Incentives, Regulation of the Incentives in Urban Policies
Bibliographic record
Abstract
The Institutional Analysis and Development (IAD) Framework developed at the University of Indiana is very promising for advancing comparative urban studies. Ostrom's “Grammar of Institutions” is useful for addressing urban allocative conflicts. Such conflicts are valuable entry points to better grasp urban politics, especially in comparative research. This paper introduces a theoretical and conceptual framework to analyse the role of monetary and non-monetary incentive schemes in the field of urban policies. The incentives to consider in urban policies can be divided into four categories: (1) direct financial incentives; (2) indirect financial incentives; (3) non-financial incentives; (4) broader social incentives. Direct and indirect financial incentives are well studied by public choices theorists in urban economics; non-financial incentives are considered in some forms of planning theory, while broader social incentives are especially stressed by urban sociologists. This paper stresses the relevance for urban theory of configurations that articulate the four kinds of incentives conjointly, from both a bottom-up perspective and a top-down perspective. Taking into account of incentives, and not only of single incentives (one by one), it is a promising advancement. It is hypothesized that this could sustain relevant development in comparative urban research.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".