Rethinking Planning Systems: A Plea for Self-Assessment and Comparative Learning
Bibliographic record
Abstract
The authors reflect on recent experiences at UN-Habitat and other international organizations to rethink the roles of planning towards larger development goals and to reform planning systems in places most in need of them. They consider the difficulties but ultimate necessity to learn from a variety of contexts and experiences to articulate general orientations for planning and planning reform which can partly transcend context. Within the variety of planning experiences, and the experiences of lack of planning, one can discern principles which can be applied in many contexts, yet those include principles of contextualization and learning. Comparative learning underpins the attempts at finding general principles, and the local application of those principles further triggers processes of learning, including comparative learning. Local and grassroots planning capacity building is vital to locally apply and contextualize international planning guidelines.
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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 | 0.143 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.072 |
| Scholarly communication | 0.024 | 0.045 |
| Open science | 0.008 | 0.027 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.010 | 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".