What Technological and Economic Elements Must be Addressed to Support the Affordability, Accessibility, and Desirability of Alternative Proteins in Low- and Middle-Income Countries?
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
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.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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