FACTORS ANALYSIS OF FOOTWEAR TRADE INDUSTRY OF INDONESIA MAIN EXPORT DESTINATION
Bibliographic record
Abstract
This study aims to identify the factor that affecting the footwear trade industry in Indonesia based on Indonesia main export destination. Understanding these factors could help leaders in trade industry institutions to better plan their strategies and further research on footwear trading. A set of data was obtained from Badan Pusat Statistic (BPS) based on data from Indonesia footwear main export destination namely: United States, China, Belgium, Germany, Japan, United Kingdom, Netherlands, Korea, Italy, Australia, Mexico, France, Canada, Denmark, Singapore, Brazil, Hong Kong, Russian Federation, Chile, Argentina and Other Countries. Exploratory factor analysis was used to identify the underlying dimensions of countries as Indonesia main export destination. By applying the factor analysis, the study will decide the number of factors to be retained and the total variance explained by these factors; the study can identify the variables in each factor retained in the final solution, on the basis of its factor loadings; the study can give names to each factor retained on the basis of the nature of the variables included in it; the study can suggest the test battery for assessing the footwear trade main export destination in Indonesia; and the study can test the adequacy of sample size used in factor analysis. The result of the study shows that KMO value is 0.784 which is > 0.5; hence, the sample size is adequate for the analysis and the commonalities of all the variables are more than .4; hence, all the variables are useful in the model. Since the variables are identified in factor 0.7 or more, the result shows that all factors which are from the year 2012-2016 contributed to the exports of footwear in Indonesia.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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 | 0.000 |
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".