Public Policy and the Business Life Cycle
Bibliographic record
Abstract
This study uses a business life cycle perspective to investigate and compare three different approaches to urban economic development policy, each of which focuses on a particular stage of company development. Well-meaning urban policies might be more effective if they were aligned with all the common stages of the business life cycle. This study compares programs that address specific stages of the business cycle to demonstrate that it is possible to address each stage. While it is not possible with a small number of cases and many dimensions of variation to validate a causal model of which packages of policies have what effects, it is possible to measure how well each type of policy is achieving its desired ends. Combining the individual perspectives into a unified whole will help entrepreneurs on their entire journey and therefore encourage and strengthen regional competitiveness. The cases include major Canadian cities that focus on early-stage capital formation, New York City, which focuses on growing existing firms, and Barcelona, which takes the unusual approach of recycling firm components when companies are on the point of failure or dissolution. The dissertation asks what theory motivates each of these economic programs, what factors these programs address, and which they miss. It concludes by drawing lessons about how these policy perspectives could be combined into a more comprehensive and resilient framework.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".