Bibliographic record
Abstract
Purpose The purpose of this paper is to see to what extent Hans-Georg Gadamer’s hermeneutic philosophy could be used to unveil how corporate discourse about financial crimes (in codes of ethics) is closely linked to the process of understanding. Design/methodology/approach Corporate ethical discourse of 20 business corporations will be analyzed, as it is conveyed within their codes of ethics. The companies came from five countries (USA, Canada, France, Switzerland and Brazil). In the explanatory study, the following industries were represented (two companies by industry): aircrafts/trains, military, airlines, recreational vehicles, soft drinks, cigarettes, pharmaceuticals, beauty products, telecommunications and banks. Findings Historically-based prejudices in three basic narrative strategies (silence, chosen items and detailed discussion) about financial crimes are related to the mindset, to the basic outlook on corporate self-interest or to an absolutizing attitude. Research limitations/implications The historically-based prejudices that have been identified in this explanatory study should be analyzed in longitudinal studies. Practical implications The historically-based prejudices that have been identified in this explanatory study should be analyzed in longitudinal studies. Historically-based prejudices could be strengthened by the way corporate codes of ethics deal with financial crimes. They could, thus, have a deep impact on the organizational culture in the long-run. Originality/value The paper analyzes the way corporate codes of ethics use given narrative strategies to address financial crimes issues. It also unveils historically-based prejudices that follow from the choice of one or the other narrative strategy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.036 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".