Market Mediators and the Trade-offs of Legitimacy-Seeking Behaviors in a Nascent Category
Bibliographic record
Abstract
Although existing research has demonstrated the importance of attaining legitimacy for new market categories, few scholars have considered the trade-offs associated with such actions. Using the U.S. organic food product category as a context, we explore how one standards-based certification organization—the California Certified Organic Farmers (CCOF)—sought to balance efforts to legitimate a nascent market category with retaining a shared, distinctive identity among its members. Our findings suggest that legitimacy-seeking behaviors undertaken by the standards organization diluted the initial collective identity and founding ethos of its membership. However, by shifting the meaning of “organic” from the producer to the product, CCOF was able to strengthen the categorical boundary, thereby enhancing its legitimacy. By showing how the organization managed the associated trade-offs, this study highlights the double-edged nature of legitimacy and offers important implications for the literatures on legitimacy and new market category formation. The online appendix is available at https://doi.org/10.1287/orsc.2017.1126 .
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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 | 0.012 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".