The Impact of Counterfeit Victims in the Victim Marketplace
Bibliographic record
Abstract
Just and efficient allocations of charity have attracted much academic and media attention.The sources of inefficiency and unjust are important to understand yet understudied.Our study aims to fill this void by directly modelling the victims' market in a collective reputation framework.By analyzing three types of individuals who signal their victim status with different personalities and incentives, we derive the honest, dishonest and unfunded equilibria as well as the mixed equilibrium where both types of these equilibria could coexist.Our analyses of the social welfare under each equilibrium shed light on key parameters that could potentially serve as policy instruments for improving social welfare.We also reveal that the mechanisms analogous to bank run and lemons market could take place in the victims' market as much as in other markets.In particular, when charity resources are scarce, more strategic signallers could rush to emit false victim signals and drive the market to the dishonest equilibrium with lower social welfare.The need for screening signallers could drive up the psychological costs of authentic victims to the extent that they voluntarily drop out of the market and suffer alone, resulting in misplaced charity funds and severe deadweight losses.When there is psychological utility associated with cheating for the hedonic signallers, the social welfare is even worse off.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 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 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".