Bibliographic record
Abstract
Purpose The concept of “value network” makes clear that the mission of business corporations cannot be isolated from three basic elements: making profits, responding to customers' needs, reacting to competitors. Too often, value networks are seen as neutral factors in the way corporate crimes are committed. Value networks are usually considered as morally neutral conditioning factors, while it is not the case. The purpose of this paper is to explain how value networks should be closely linked to any crime prevention system. Design/methodology/approach Christensen's notion of value networks will be used in order to see if corporate crimes constitute a dysfunction of value networks. The most important “traditional” antecedents of corporate crime will be analyzed. Findings Both financial performance and growth rate are reflecting a deep concern for profitability. The level of market concentration not only reveals the structure of the market itself but also the way competitors react one to each other (particularly, through mergers and acquisitions). In both cases, what is unveiled is the capacity of value networks to enhance ethical as well as unethical practices. The way competitors react one to another, as component of the industry concentration, actually reveals how value networks are morally unsettled when such reaction could influence organizations to commit corporate crimes. Originality/value The originality of this paper is to reveal how changing corporate culture could redefine the moral boundaries of value networks within the organization.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| 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".