Industrial Development Through Tacit Knowledge Seeding: Evidence from the Bangladesh Garment Industry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We explore how the establishment of an industry pioneer through foreign seeding of industry knowledge can subsequently catalyze the growth of a developing country’s industry by involuntarily propagating the knowledge to subsequent entrants. As industry knowledge has tacit elements, we focus on mechanisms that enable experienced workers from the pioneer to seed the knowledge to new entrants. We examine the relationship between entrants’ characteristics and the mechanisms exploited to access the industry knowledge, and the impact of the mechanisms exploited on firm performance. Empirical findings from two historical episodes in the Bangladesh garment industry suggest that industry knowledge seeding was essential for the initial establishment and subsequent expansion of the industry. Our paper highlights the role of experienced workers’ mobility in building new firm capabilities and provides novel insights into industrialization in developing economies. This paper was accepted by Bruno Cassiman, business strategy.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it