The Conundrums of the Estimated Magnitude of Food Waste Generated in South Africa
Bibliographic record
Abstract
This paper presents three conundrums that influence the estimation of the magnitude of food waste generated in South Africa.The first conundrum is the lack of standard definition of food waste which includes the inconsistencies and interchangeable use of food waste and food loss.The second conundrum relates to inconsistencies associated with the inclusion and exclusion criterion of inedible portions into food waste, and lack of clarity about the stages in the food supply chain at which food losses are considered food waste.The last conundrum relates to the credibility of sub-Saharan Africa's assumptions and methodological replicability used in the estimation of South Africa's magnitude of food waste generated.This paper highlights the influence of the three conundrums and relationship between qualitative and quantitative measurement of food waste variable by recalculating the food waste generation using the 2007-2009 data.Ultimately, the study results confirm that scientific quantification of variables should be based on clearly defined and validly demarcated qualitative variables to prevent methodological replicability and validity errors, as evident from the three conundrums identified in the South African food waste estimates.
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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 | 0.044 | 0.138 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".