A Cross-Cultural Classification of Service Export Performance Using Artificial Neural Networks
Bibliographic record
Abstract
Behavioral determinants and their relationship to exporting performance have been examined in varying capacities for the last several decades, although very few, in that time have examined them in a cross-cultural context. The offer here is a firm level examination of variables with a particular effort to validate performance measurements across cultural settings. Specifically, a sample of 1,246 exporting service firms from Japan, Germany and the United States are empirically analyzed in an attempt to answer the following primary questions: (a) Does a common set of high-ranking export determinants for Exceptional Exporters exist among service firms from the examined countries, combined? and furthermore, (b) Does a unique set of high-ranking export performance determinants for Exceptional Exporters exist within the service firms from each country, individually? An artificial neural network is selected as the statistical method because of the unique perspective it provides when examining a highly non-linear function with many variables, offering results that consistently prove to numerically approximate such functions much easier than conventional methods, along with the ability to dependably and accurately predict membership classification while providing weighted analyses of input variables.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 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 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".