How Does Aversion to Insects Intersect with Sustainable Production and Consumption?
Bibliographic record
Abstract
Abstract Damage by insect pests reduces the quantity and quality of harvestable food, making pest management a major challenge for sustainable food production. Perception of insects in association with food is known to cause negative emotions including disgust and fear. These negative emotional responses are likely to alter perception and reduce enjoyment of food. Here, we introduce the concept of insect pests and highlight some of the ways that pests reduce the quality and quantity of harvestable food. Next, we discuss the different sensory properties of food, emotional responses to food and how different factors can impact humans’ perception of food products. Finally, we share the results of our study where the intersection of disgust and sensory perception is explored through an exploration of the blueberry maggot ( Rhagoletis mendax ), a fly that lays eggs in ripening blueberries. Collectively this work demonstrates that merely learning about the existence of an insect pest negatively impacts the perception of fresh fruit, in terms of taste characteristics, and emotional response. These results reinforce the necessity of effective pest management to ensure that consumers maintain a positive affinity towards foods. Information © The Authors 2024
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".