Bibliographic record
Abstract
Purpose: Previous studies on traditional markets in Korea focuses on facility modernization, support systems, and service quality. Studies on overseas markets are mainly European cases. Therefore, the purpose of this study is to review the literature on the development process of traditional markets in Canada and to analyze the cases of representative markets to derive implications for the establishment of marketing strategies for traditional markets in Korea. Research design, data, and methodology: Based on the literature study on the development process of Canadian traditional markets, two markets were selected as cases, and literature review and case analysis were carried out in parallel. The case markets were investigated by the researcher through three visits and interviews. The main analysis was limited to the development process of each market and the current retail mix factors. Results: Canadian traditional markets began in 1800 when the concept of a European market was introduced to early settlements. It went through a process of rise and fall due to various environmental changes, and since the 1970s, its value has been recognized for its differentiated quality and continues to grow. As a result of the case study, Granville Island Market differentiated itself with a retail mix that reflected the characteristics of a high proportion of tourists. St. Lawrence Market emphasizes the differentiation of retail mix strategy centered on local residents. Implications: As a result of the analysis, it is necessary to accurately identify the city where the market is located, trading area, location, and customer characteristics, and differentiate marketing strategies centered on retail mix factors based on this. In particular, it is necessary to differentiate the composition ratio of each sector to strengthen competitive sectors by reflecting the unique characteristics and culture of each region and market in the assortment of goods and service facilities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".