The Shadow of the Consumer: Analyzing the Importance of Consumers to the Uptake and Sophistication of Ratings, Certifications, and Eco-Labels
Bibliographic record
Abstract
Why has the market uptake and sophistication of information-based environmental governance (IBEG) programs like eco-labeling increased despite mixed signals on the willingness and ability of individual consumers to support such programs? We argue that the extant literature on IBEG focuses too narrowly on individual consumer purchasing decisions to the exclusion of other mechanisms through which consumers, both as individuals and as an imagined collective, exert influence. As a corrective, we present a novel conceptual framework that highlights the different causal mechanisms through which consumers contribute to the uptake and sophistication of IBEG. We call our framework “the shadow of the consumer” since it suggests a more latent and indirect role for consumers than voting-with-one’s-wallet. Our analysis adds nuance and complexity to accounts of consumer agency vis-à-vis environmental ratings, standards, certifications, and eco-labels and helps explain the proliferation and growing sophistication of such programs despite the variability of individual consumer support.
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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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".