A Matter of Trust: How Trust Influence Organic Consumption
Bibliographic record
Abstract
This article shows that trust in the organic label as well as perceived positive health effects of consumption of organic products have positive causal effects on actual organic consumption. Furthermore perceived positive environmental effects and perceived better animal welfare related to organic production are found not to have no significant causual effect on actual behaviour, whereas concern for artificial additives and low price sensitivity have. Even when differences in time varying attitudes have been controlled for there is still a rather large heterogeneity in the organic purchasing behaviour. Part of this heterogeneity can be explained by differences in urbanisation or level of education, while income does not seem to have any effect when education has been controlled for. The data used is panel data for 830 households reporting actual purchases as well as stated preferences and attitudes in 2002 and again in 2007. The results point towards that the most efficient way of increasing organic consumption seems to be to continuously increasing the trust in the organic label and/or to document the positive health effects of organic food by e.g. focussing on measurable things such as a lower frequency of findings of pesticide residues in organic foods compared to conventional foods.
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
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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".