Challenging the Sustainability Narrative: Unveiling the hidden socioecological costs of plant-based alternatives.
Bibliographic record
Abstract
This thesis investigates the hidden socio-ecological costs associated with plant-based alternatives, with a particular focus on avocados produced in Michoacán, Mexico. This study employs a convergent mixed-methods design using three distinct components: Lifecycle Analysis (LCA), Emergy Analysis (EmA) and Political Ecology (PE). The LCA revealed a carbon footprint of 5.96 kg CO₂e per 1kg avocado, placing avocado production on par with other high-impact animal food sources, such as poultry. The EmA results revealed an Emergy Yield Ratio (EYR) of 1.007, an Environmental Loading Ratio (ELR) of 153.14, and an Emergy Sustainability Index (ESI) of 0.0066. This is indicative of an unsustainable system. The Political Ecology (PE) analysis revealed a weak state apparatus, an ever-increasing infiltration of criminal actors resulting in land dispossession, para-autonomous governance structures and fragmented resistance movements. Together, these findings question the dominant sustainability narratives and underscores the need for more multidimensional frameworks to better evaluate sustainability. In response, this study introduces the Integrated Socio Ecological Lifecycle Assessment (ISELA) framework as a novel approach for sustainability assessments. The ISELA framework integrates LCA, EmA and PE, treating each component as equally significant in understanding the full range of socio-ecological impacts. The framework offers an alternative to techno-centric models, emphasizing the interdependence of environmental burdens, resource efficiency and power dynamics in food-systems.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".