Untangling Searchable and Experiential Quality Responses to Counterfeits
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
In this paper, we untangle the searchable and experiential dimensions of quality responses to entry by counterfeiters in emerging markets with weak intellectual property rights. Our theoretical framework analyzes market equilibria under competition from counterfeiting as well as under monopoly branding. A key theoretical prediction is that emerging markets can be self-corrective with respect to counterfeiting issues in the following sense: First, counterfeiters can earn positive profits by pooling with authentic brands only when consumers have good faith in the market (i.e., they believe there is low probability that any product is a counterfeit). When the proportion of counterfeits in the market exceeds a cutoff value, brands invest in self-differentiation from the competitive-fringe counterfeiters. Second, to attain a separating equilibrium with counterfeiters, branded incumbents upgrade the searchable quality (e.g., appearance) of their products more and improve the experiential quality (e.g., functionality) less compared with monopoly equilibrium. However, in the pooling equilibrium with sporadic counterfeits, authentic firms instead may invest in experiential quality to attract more of the expert consumers who are well versed in quality. This prediction uncovers the nature of product differentiation in the searchable dimension and helps with analyzing real-world innovation strategies employed by authentic firms in response to entries by counterfeit entities. In addition, welfare analysis hints at a nonlinear relationship between social welfare and intellectual property enforcement.
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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 | 0.016 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it