Gifting, ridding and the “everyday mundane”: the role of class and privilege in food waste generation in Indonesia
Bibliographic record
Abstract
Current research on household food waste has not considered the impact of the inter-class power dynamics between employers and domestic helpers on food waste management and generation in the Global South. This article focuses on the issue of food justice and will demonstrate that it is important to reconsider household food practices within the framework of unequal power dynamics in household units – especially between employers and their domestic helpers. Informed by food waste regime conceptual framework, this paper will examine the complex food provisioning practices of Indonesian households. The research draws on 21 in-depth interviews with households of varying incomes, multiple site visits, participant observation and going along on grocery trips to better understand the power dynamics and practices that result in, or prevent the generation of household food waste. In addition, 12 key informant interviews with government officials, traditional food vendors, supermarket managers, and a waste collector was also conducted. In an Indonesian context, understanding the interclass dynamics of the household, namely, who gets to define what is “food” and what is “waste” is key to understanding the broader phenomenon of food waste in order to promote solutions to food waste prevention and food insecurity that is socially and environmentally just.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".