The Propensity to Trust Others: Gender and Country Differences
Bibliographic record
Abstract
Our research examines the level of individual trust in others,which is an important issue because it essentially determines the level ofadditional work that must be done by auditors to make their audit decision. This study includes the responses of315 accounting students from Afghanistan, Australia,Nepal, and the United States. It then examines whether levels oftrust vary by country. Our dataindicate that students fromboth Australia and Nepal had significantly different levels oftrust than the students from the United States (our control group).While the students from Nepalhad a significantly lower level of trust than the students from the United States, the students from Australia had a significantly higher level oftrust than the students from the United States. Additionally, male(female) students indicated a lower (higher) level of trust. Our findingof differences among countries in the level of individual trust has implicationsin the field of international auditing. While the countries of the world areworking at harmonizing their accounting and auditing standards, differences inthe amount of work actually done on an audit could be determined by the levelof trust in a client. These countries provide a contrast among the scores forTransparency International’s Corruption Perceptions Index and Hofstede’scultural constructs of individualism and uncertainty avoidance; the sampleincludes Afghanistan and Nepal that are not presently included inauditing-related research.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".