Linking seaport activity and regional economy: an analysis at the level of Japanese prefectures
Bibliographic record
Abstract
This paper presents a comprehensive analysis of the links between seaport activity and regional manufacturing in Japan during the 1990-2010 period. Through an analysis of changes on regional share, it shows that at regional scale, changes in port throughput are highly linked with changes in manufacturing employment. Hence shrink in port throughput has much more affected the main metropolitan regions than the rest of Japan. However many regions deviate from this general trend. In a second step, using the existing literature and some detailed data, we explain some of these deviating figures. Regional specificities and the strategies of manufacturing firms and shipping lines seem to influence the intensity of throughput changes. We argue that the intensity of the shrink in the ports located in the metropolitan regions is due to shifts in manufacturing (metropolitan regions play new roles inside the system of production). Through other cases, we show that some regions specialized in niches in manufacturing might reduce the impact of the crisis, or at least make it less dependent upon national dynamics. Our study also shows that while some manufacturing activities generating heavy and large cargo flows tend to be tightly connected to the seaport city and region, other manufacturing activities, which are highly regionally specialized and mainly involved in international horizontal division of labor, are less connected.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".