Social Labeling by Competing NGOs: A Model with Multiple Issues and Entry
Bibliographic record
Abstract
In many settings firms rely on nongovernmental organizations (NGOs) to certify prosocial attributes embodied in their products. We provide a model of competition between NGOs in the provision of labeling services. Competition between a fixed number of NGOs features a “race to the top” in labeling standards, but entry of NGOs offering new labels pushes standards down. In a wide range of settings NGO entry and competition results in too many labels being adopted, with each label being too stringent. Compared to a setting in which firms can credibly communicate the social attributes of their products, labels demand greater prosocial behavior than is desired by firms, although with proliferation of the number of labels this discrepancy disappears. In contrast to existing models, firms may engage in excessive corporate social responsibility when they rely on an NGO as a certifying intermediary. This paper was accepted by Bruno Cassiman, business strategy.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.018 | 0.009 |
| Insufficient payload (model declined to judge) | 0.054 | 0.005 |
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".