What Technological and Economic Elements Must be Addressed to Support the Affordability, Accessibility, and Desirability of Alternative Proteins in Low- and Middle-Income Countries?
Bibliographic record
Abstract
Populations in low- and middle-income countries (LMIC) typically consume less than the recommended daily amount of protein. Alternative protein (AP) sources could help combat malnutrition, but this requires careful consideration of elements needed to further establish AP products in LMIC. Key considerations include technological, nutritional, safety, social, and economic challenges. This perspective analyzes these considerations in achieving dietary diversity in LMIC, using a combination of traditional and novel protein sources with high nutritional value, namely soy, mycoprotein and cultivated meat. Technological approaches to modulate the techno-functionality and bitter off-tastes of plant-sourced proteins facilitate processing and ensure consumer acceptance. Economic considerations for inputs, infrastructure for production and transportation represent key elements to scale-up AP. Dietary diversification is indispensable and LMIC cannot rely on plant proteins alone to provide adequate protein intake in a sustainable way. Investments in infrastructure and innovation are urgently needed to offer diverse sources of protein in LMIC. This perspective assesses the current technological, economic, and social factors needed to effectively establish diverse alternative dietary proteins, including plant-based proteins, mycoproteins, and cultivated meat, and mitigate protein deficiency in low- and middle-income countries.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".