Organizational geography, experiential learning and subsidiary exit: Japanese foreign expansions in China, 1979-2001
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 examine how experiential learning and vicarious learning, as tied to a subsidiary’s organizational geography, influence the exit rates of Japanese subsidiaries located in China. We find that exit rates were lower for subsidiaries that were established geographically proximate to the prior expansions of industry peers from Japan. Exit rates were also lower for subsidiaries established by firms with experience in similar product markets in China. Exit rates were greater, however, when a parent firm had substantial experience outside the product market of the current expansion. Importantly, the influence of a subsidiary’s geographic proximity to its peers on its exit rate is contingent on whether its parent firm had prior experience inside or outside the product market of the new expansion.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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