Edible insects as foods: mapping scientific publications and product launches in the global market (1996-2021)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Edible insects are gaining interest for their health and environmental merits as human food. Within this framework, the main objectives of this research are to fill the gap between market trends and scientific research about the status of edible insects in foods, suggest a roadmap for future research and boost product launches. For these reasons, an attempt has been made to review the progress of scientific documents related to edible insect foods and to detect the prominent trends in insect-based foods during the period 1996-2021. By putting the findings of these searches together, we were able to observe that scientific publications have increased exponentially since 2015 – similar to product launches but at a higher speed. Europe was found to be the most prolific region in terms of publications and food product numbers due to increased awareness of the benefits of insects. Market data offered insights into the main selling countries, food applications and insect ingredients. In the future, food formulators will still have to find innovative solutions to offer insect-based foods with pleasant flavours and textures and, in turn, contribute to healthy and sustainable gastronomy. Ensuring safety and setting a clear legislative framework will further organise the sector and thus boost edible insects as a future food commodity.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it