Survey Methods: How Planning Practitioners Use Them, and the Implications for Planning Education
Bibliographic record
Abstract
This article is concerned with two aspects of how planning practitioners use survey-derived data; how planners integrate the limitations of survey questionnaires into practice, and the prevalence of such data within planning. Using a web survey ( n = 201) and interviews ( n = 18) of Canadian municipal planners, I find that survey data are heavily relied on, but many planners do not seem to be aware of cognitive biases when designing surveys, and those that are, have little knowledge of how they ought to mitigate them. To develop planners’ understanding of these biases and improve the survey data they collect, quantitative methods courses within planning curricula could respond by expanding beyond statistical analysis to incorporate survey design and “the total survey error approach” of survey methodology.
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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 | 0.470 | 0.568 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.017 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".