The Economic Impact of the Suitcase Trade on Foreign Trade: A Regional Analysis of the Laleli Market
Bibliographic record
Abstract
Previous studies have analyzed the suitcase trade from global, state-centric and local perspectives. While the first two categories of studies analyzed the economic impacts of the suitcase trade from global and state-centric perspectives, other studies analyzed the cultural implications of the suitcase trade. This is the first systematic study to analyze the economic impact of the emergence, increase and decrease of the suitcase trade on suitcase traders. Specifically, this study analyzes the dynamics of the suitcase trade between Turkey, the Russian Federation, the former Soviet Republics on foreign trade. Individuals from different sectors (wholesalers, retailers and manufacturers) constitute the target group of this study. Surveys and focus group interviews serve as our data. The data covers the period from 1990 to 2013. Surveys were completed by 257 people from firms which participated in the suitcase trade in the Laleli market-Turkish market place for the suitcase trade. Focus group interviews were conducted with 16 people in a conversational style. This exploratory study contributes to the body of empirical evidence by analyzing the changing dynamics of the suitcase trade. The study concludes with policy proposals to tackle local, regional and global challenges of the suitcase trade.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".