Understanding Local Economic Development, Second Edition
Bibliographic record
Abstract
This book offers insights into the process and the practice of local economic development. Bridging the gap between theory and practice, it demonstrates the relevance of theory to inform local strategic planning in the context of widespread disparities in regional economic performance.The book summarizes the core theories of economic development, applies each theory to professional practice, and provides detailed commentary on them. This updated second edition includes more recent contributions—regional innovation, agglomeration, and dynamic theories—and presents the major ideas that inform economic development strategic planning, particularly in the United States and Canada. The text offers theoretical insights that help explain why some regions thrive while others languish and why metropolitan economies often rise and fall over time. Without theory, economic developers can only do what is politically feasible. This book, however, provides them with a logical tool for thinking about development and establishing an independent basis on which the local consensus needed for evidence-based action undertaken in the public interest can be built. Offering valuable perspectives on both the process and the practice of local and regional economic development, this book will be useful for both current and future economic developers to think more profoundly and confidently about their local economy.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.007 |
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".