Proposal for a Novel Method to Eradicate Scalpers of Otaku Goods
Bibliographic record
Abstract
With the recent expansion of the otaku goods market, the problem of individuals and dealers buying up limited-edition products and reselling them at exorbitant prices (“scalpers”) has become a serious concern, placing an economic burden on fans and causing market distortion. Measures against resale, such as determining buyers on a first-come, first-served basis or by lottery, are common; however, their effectiveness is limited because scalpers can buy up all the products using swarm tactics. This problem can be solved by selecting buyers through an auction system, but in practice this method is rarely used. This study examines the reasons why the auction method is not easily adopted and proposes a new method to eliminate scalpers. The auction method, which seeks a fair market price, is difficult to adopt because sellers prefer to deliver products to those who deeply appreciate their work rather than those willing to pay the highest price, a characteristic often associated with otaku goods. Therefore, it is necessary to develop a system that facilitates product ownership for individuals with a strong appreciation for the work. This study proposes embedding information media, such as microchips, into products to register ownership details. Owners could then access services such as bonus videos and images and receive email notifications marking the anniversary of their acquisition. We believe that unfair price inflation by scalpers can be suppressed by updating the owner’s information, provided the product is traded exclusively within this system, which may restrict reselling for a certain period after the transaction.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".