Social life cycle assessment of calves in Mexico and identification of barriers in the use of a generic database
Bibliographic record
Abstract
Abstract Purpose Social impacts regarding animal-based food are on the global agenda for sustainability development, especially due to reoccurring problems related to human rights, labor rights, decent work, and indifference to farm animal welfare. Social life cycle assessment (S-LCA) is considered an ideal tool for understanding social problems that may arise in the value chains of products and services. This study aims to (1) assess the social risks and opportunities associated with calf rearing using a generic database and (2) analyze the barriers of a generic database applied to S-LCA of animal-based food. Methods An S-LCA was carried out in the livestock sector, using midpoint indicators employing the Product Social Impact Life Cycle Assessment (PSILCA) database, based on 49 indicators. The functional unit was defined as producing 0.39 kg of live-weight calf in Mexico, a quantity corresponding to 1 USD necessary to assess the impacts with the PSILCA database. OpenLCA software version 1.10, 2020 was used to model the product system, incorporating foreground and background processes from the PSILCA database v.2. The results were analyzed to identify the critical indicators missing in the study, and the relevance of their inclusion was discussed. Results and discussion The highest impacts found are related to “trade unionism,” “certified environmental management system,” “sanitation coverage,” “public sector corruption,” and “drinking water coverage,” impacts that coincide with other studies of S-LCA in the agricultural sector. From the analysis of results, some limitations were identified in using the PSILCA database in animal-based food, such as the required granularity to discern slight differences between production systems, which can reduce understanding of the social implications in a differentiated way. Furthermore, indicators of the ethical treatment of animals and farm crime can be crucial in the agricultural sector in Latin America; therefore, these must be included in the social sustainability analysis of animal-based food. Conclusion The use of the PSILCA database highlighted key social risks associated with calf rearing in Mexico, specifically in relation to “safe and healthy living conditions” for the local community and “health and safety” for workers. However, the limitations of the PSILCA database, particularly its lack of granularity for the agricultural sector in the Latin American region, suggest the need for further interdisciplinary research. By integrating more region-specific knowledge and enhancing the database’s granularity, the evaluation of non-intensive livestock systems can be significantly improved, allowing for a more accurate representation of social sustainability in this context.
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 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".