Bibliographic record
Abstract
Purpose The purpose of this paper is to describe corruption, fraud and cybercrime as dehumanizing phenomena. Design/methodology/approach Berdiaeff's notion of slavery and Sartre's concepts of lie and bad faith are used in order to put light on the dehumanizing effects of corruption, fraud and cybercrime over social life itself. Findings Corruption, fraud and cybercrime constitute dehumanizing processes insofar as they undermine mutual trust among people. When they arise in the organizational setting, corruption and fraud (committed through cyberspace or any other means) are institutionalizing suspicion and creating a deep loss of mutual trust and confidence within the organization. Human relationships within a corrupt and fraudulent organization are harder to develop than in a workplace characterized by honesty and integrity. Research limitations/implications The paper is focusing on Berdiaeff's notion of slavery and Sartrian concepts of lie and bad faith. It does not reflect all aspects of dehumanizing phenomena such as corruption, fraud and cybercrime. Practical implications The analysis reveals the way in which Sartrian concepts of lie and bad faith could be applied to the behavior of corrupt and fraudulent people as well as cybercriminals. Social implications Owing to the transnational nature of both corruption, fraud and cybercrime, such phenomena negatively affect the potentialities to develop a cross‐cultural and interreligious dialogue on the international scene. Originality/value The originality of the paper is that it reveals that the way an organization could fight corruption, fraud and cybercrime could be determined by its propensity to tolerate lies and bad faith in its organizational culture.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.036 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".