The Planning — Policy Connection in US and Canadian Economic Development
Bibliographic record
Abstract
The evidence regarding the extent of planning for local economic development decisions is mixed, and there is a general dearth of studies that examine whether rational planning is tied to policy choices in any systematic way. The research reported here rests on two premises. The first is the simple assumption that for planning to play an integral part in the economic development policy process there should be some empirical connection between local economic development goals—what officials want to achieve—and the economic development strategies they pursue. The second premise, based on extant literature, is that Canadian cities have different and more institutionally integrated planning traditions and practices than those in the USA, and hence should evidence a closer link between development goals and policies. Based on a survey of all of the municipalities in the USA and Canada with populations over 10 000, it is concluded that for US cities goals do not appear to correlate with discrimination among economic development policies; that is, planning is not related to the selection of activities. On the other hand, in Canada, cities' goals are related to specific policies in a more logical or rational fashion. In short, it appears that economic development planning is tied to the selection of policies to a greater extent in Canadian cities than in the USA.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.017 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".