Strategic blockchain adoption to deter deceptive counterfeiters
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Counterfeiting is an ever growing problem worldwide which is exacerbated by the ease of access through e-commerce and online shopping. This calls for innovative technologies, such as blockchain, to identify, track, and prevent fake products from reaching consumers, especially for vital sectors such as the drug industry, which is the main motivation for this work. We investigate the strategic implications of using blockchain technology to deter counterfeiters. We particularly focus on the case of deceptive counterfeits that infiltrate legitimate distribution channels. Deceptive counterfeits lack the quality of genuine products and may pose immense health and safety risks to consumers who are unable to distinguish them from genuine products at the time of purchase. In contrast to prior literature that assumes that blockchain eliminates deceptive counterfeiting, we present a model that realistically considers blockchain as a technology that increases the capability of detecting counterfeits. This capability nonetheless comes at an increasing cost that may financially discourage genuine manufacturers from adopting the technology. The presented model shows that blockchain is not always financially beneficial and demonstrates that manufacturers can strategically balance between product quality and investment in blockchain to combat counterfeiting. Furthermore, our results demonstrate that, with the availability of blockchain, genuine manufacturers may be less interested to differentiate products based on quality, but rather rely on blockchain to block counterfeits.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it