ECONOMIC DEVELOPMENT QUARTERLY / November 2001Reese, Rosenfeld / QUESTIONING CONVENTIONAL WISDOM FORUM Yes, But...: Questioning the Conventional Wisdom About Economic Development
Bibliographic record
Abstract
This article explores various aspects of the conventional wisdom regarding local eco-nomic development policy and policy making. Much widely accepted knowledge about why and how local governments approach economic development is based on a fairly narrow array of methodological approaches. And the conclusions often suffer from the limitations inherent in their respective methodologies. This may mean that what we think we know about local economic development might be reasonably accurate, but it does not quite reflect actual local dynamics. Yes, the conventional wisdom is true, but.... Using data from a large survey database of Canadian and U.S. cities along with findings from nine case studies in medium-size and smaller cities in both nations, the authors challenge a number of accepted truths and present an alternative civic cul-ture framework for better understanding economic development policy making. This article questions various aspects of the conventional wisdom regarding local economic devel-opment policy and policy making. Much widely accepted knowledge about why and how local governments approach economic development is based on research employing a fairly narrow array of methodologies. And the findings and conclusions of that research often suffer from the
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".