Network Catalysts to Small Businesses: A Strategy for Fragmented Industries
Bibliographic record
Abstract
The catalyst roles played by the New York City's Garment Industry Development Corporation (GIDC) and San Francisco's Garment 2000, two non-profit organizations providing services to the apparel industry, were analyzed using a network perspective. The networks of apparel businesses in the two cities were advanced by GIDC and Garment 2000 through their structures and strategies, including building trust, providing easy entry and exit mechanisms to network members, offering dynamic programming, and establishing partnerships with other organizations serving the apparel industry. The loosely structured business networks in the apparel industry and other fragmented industries benefit from catalyst organizations that can increase communication across the memberships, identify and address needs of member businesses, and seek funding to support programs. Implications for businesses that want to initiate catalyst organizations to serve small business networks in other industries are discussed.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".