High-profile corporate scandals based on the agenda-setting theory: analysing developed and emerging countries
Bibliographic record
Abstract
Studies indicate that more developed countries, compared to emerging countries, have more corporate scandals, which seems counterintuitive, contradicting common sense assumptions. Furthermore, we identified a gap in the methodological application of data collection when we found a more comprehensive method. In this sense, this work has two objectives: firstly, propose a method that improves the data collection process based on the agenda-setting theory; and secondly, based on this new method, collect, categorize, and analyze cases of high-profile corporate scandals in developed and emerging countries, comparing and discuss the results. With a sample ranging from 2010 to 2021, that includes Brazil, Canada, South Korea, Spain, India, Italy, and Mexico, and with the preliminary intention of understanding the phenomenon, we used contingency tables, frequency analyses, percentage distribution, and comparative analyses through charts. Our results suggest that emerging countries have more scandals, contradicting the preliminary findings; that no significant difference exists between the scandals of the studied groups; and that the collection model was validated as more comprehensive. The scandals’ profile and their prominent characteristics were also outlined. This study provided valuable insights into the dynamics of corporate scandals in developed and emerging markets. Our study provides insights into corporate scandal patterns in developed and emerging markets, helping investors, regulators, and governments better understand corporate scandal patterns and potentially leading to more transparent and ethical market functioning.
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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 | 0.012 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".