Investigation of the relationship between the levels of public trust and corruption: A topological model and statistical methods
Bibliographic record
Abstract
The article analyzes the concepts of "trust" and "corruption of society" and explores the interdependence between them using the international indexes Edelman Trust Barometer (ETB) and Corruption Perceptions Index (CPI). The research methods are: 1) the topological model proposed by the authors, based on the averaged values of these indices; 2) correlation and regression analysis; 3) interval method (studying the dependence on the selected intervals of the averaged values of ETB and CPI). The topological model uses data on the values of these indices for 28 countries and was developed in two versions for the time periods (2011–2021) and (2013–2019). In both cases, the model gives a similar picture of the points placement on the coordinate plane, which indicates the stability of the results. A comparison of empirical data over the time periods revealed countries with a relatively stable ratio of ETB and CPI indices (Germany, Indonesia, Canada, Colombia, France) and countries where this ratio is noticeably changing (Australia, Argentina, Brazil, Hong Kong, Italy, the Netherlands). The analysis of the topological model was carried out and zones of low, medium and high levels of corruption were identified. Zones are characterized by a specific type of relationship between ETB and CPI indices. The general type of dependence of the level of trust on corruption in the form of a horizontal S-shaped curve is approximately determined. Regression analysis using time series data for 28 countries was performed. Statistically significant linear regression equations (reflecting the relationship between the considered economic categories) were obtained for some countries (the Netherland, Sweden and Japan). The conclusion about the significant nonlinearity of the studied relationship is made. This is evidenced by both the visual analysis of the topological model and the results of regression analysis conducted for the selected intervals of change values of the ETB and CPI indexes.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".