Commentary on the Absence of Experiments in Planning
Bibliographic record
Abstract
A recent review by Jacques Du Toit, Nelius Boshoff, and Niclesse Mariette finds that planners rarely use experimental research designs. This commentary examines why experimentation is rare in planning and discusses the legitimate challenges involved with conducting experiments in a planning context. In doing so, we aim to foster a discussion on the feasibility of conducting experimental research in our field. While not all planning policies are amenable to experimentation, we see untapped potential in many areas of planning, especially on questions of public engagement. Collectively we hope that planners can begin to identify more instances in which experimental research is possible.
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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 | 0.077 | 0.225 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.026 |
| Scholarly communication | 0.008 | 0.017 |
| Open science | 0.013 | 0.006 |
| Research integrity | 0.045 | 0.067 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".