Spoils from the Spoiled: Strategies for Entering Stigmatized Markets
Bibliographic record
Abstract
Abstract Stigmatized markets are those where either the products/services, or the consumers, or both, have been collectively, negatively stereotyped and devalued by one or more stakeholder audiences in ways that discredit the overall market. Many stigmatized markets exist, and many flourish, yet little systematic attention has focused on entry into such markets. Our article addresses this gap by conceptualizing various strategies for entering stigmatized markets. We further present propositions regarding the market‐level factors that can influence which of these strategies firms will choose to employ. The contributions include: conceptually clarifying the nature of stigmatized markets; identifying additional types of entry strategies relevant for entering stigmatized markets; theorizing the conditions under which firms would choose one entry strategy over another; and opening up for consideration the effects that market entry may have on stigmatized actors in targeted markets.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".