Comparative Planning Research, Learning, and Governance: The Benefits and Limitations of Learning Policy by Comparison
Bibliographic record
Abstract
In this article, the authors develop a perspective on the value of, and methodologies for, comparative planning research. Through comparative research, similarities and differences between planning cases and experiences can be disentangled. This opens up possibilities for learning across planning systems, and possibly even the transfer of best planning and policy practices across systems, places, or countries. Learning in governance systems is always constrained; learning in planning systems is further constrained by the characteristics of the wider governance system in which planning is embedded. Moreover, self-transformation of planning systems always takes place, not always driven by intentional learning activities of individuals and organizations, or of the system as a whole. One can strive to increase the reflexivity in planning systems though, so that the system becomes more aware of its own features, driving forces, and modes of self-transformation. This can, in turn, increase the space for intentional learning. One important source of such learning is the comparison of systems at different scales and learning from successes and failures. We place this comparative learning in the context of other forms of learning and argue that there is always space for comparative learning, despite the rigidities that characterize planning and governance. Dialectical learning is presented as the pinnacle of governance learning, into which comparative learning, as well as other forms of learning, feed.
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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 | 0.168 | 0.256 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.007 | 0.062 |
| Scholarly communication | 0.017 | 0.041 |
| Open science | 0.006 | 0.018 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".