Measuring National Character Toward Developing A Research Method for International Accounting Studies
Bibliographic record
Abstract
This study measures national character in seven developed countries, based on social capital concept. Evaluating national character in developed countries help cross-country study on accounting system. The measurements of national character use data of the World Values Surveys (WVS) conducted by the World Values Surveys Association. The WVS is a questionnaire survey that uses a random sampling method with multiple precoded selections. Compared to other social capital surveys, this survey makes better measurement of national character because it includes numerous questions in a wide range of fields and focuses on many people in diverse countries. Factor analysis of the WVS data identifies three factors of social capital concept. These three factors are consistent with the components of social capital concept proposed in previous studies. Structural equation model finds the coefficients for measuring national character, and regression analysis measures three indexes of national character of each country. The findings are as follows. Social capital consists of three factors such as social trust, religious social norms, and political networks. The measures of these three factors are the lowest in Japan, followed by France, the United States, Germany, Canada, and Australia, in increasing order. In developed countries, religious social norms measures are negative and low, and the effect of political networks on national character is relatively low. This study implies that differences in national character affect various national institutions and systems. This study has significant implications for both regulators and financial markets.<br>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".