A Learning-Centered View of Business Associations: Building Business-Government Relations for Development
Bibliographic record
Abstract
The problems of rent seeking and state captured by business associations have been prominent among the concerns of economic development theory. This paper argues that firms and the state can make possible the building of new institutions that foster improvements in economic performance through arrangements that emphasize goal setting, problem solving, and continual evaluation of progress toward defined goals. The paper reviews key ideas on the learning-centered approach and builds on them to analyze the kinds of government–business relations that contribute to economic development. It uses case study material based on Chile's agro-industry business association FEPACH. It illustrates how innovative state policy coupled with private firms' efforts led to the discovery of group-based coordination that fostered rapid diffusion of new technology and production organization among Chilean enterprises. This work discusses the institutional reshaping of the business association and business–state relations to encourage learning and advance a process of development.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.042 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".