Picking Apples: Can Multi-Attribute Ecolabels Compete?
Bibliographic record
Abstract
Abstract Global food markets in Europe, the U.S. and elsewhere, are experiencing a rapid growth in the number of private party and government environmental labeling programs. Most current ecolabels are defined by standards related to multiple environmental practices. This study presents an analysis of consumers’ choice of food products, in this case apples with or without ecolabels, where the ecolabels present varying combinations of farm practices with implications for environmental quality. These practices include: whether or not standards are met specific to on-farm pest management; presence of stream or groundwater quality protection; presence of on-farm wildlife habitat provision; and which certifier provides the guarantee. Factors influencing consumer preferences for ecolabel attributes are evaluated as a choice-based conjoint analysis. To empirically test the effect of heterogeneity of consumers on preferences for ecolabel attributes, surveys were conducted in a stratified sample in three regions (Portland, Oregon; Minneapolis, Minnesota; Rhode Island) with a focus on sampling across shoppers at different types of markets including conventional supermarkets, farmers markets, natural food stores and food co-ops. Results show that preferences for ecolabels are most strongly driven by type of pesticide usage, in particular for non-synthetic pesticides which were identified with organic production. With an appropriate price premium, ecolabels with an alternative pest management practice and other environmental practices were preferred to conventionally produced apples. These results varied according to age and gender of respondents, and type of store at which respondents shopped.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".