Bibliographic record
Abstract
Abstract In order to meet the 2015 Paris Agreement for 1.5°C global warming, per capita emissions need to come down to 2.9 tonnes by 2030. Food systems are known to be a significant source of an individual’s carbon footprint and demands attention in sustainability management. The objective of this research is to conceptualise and define an intersection between contagion theory and socio-ecological systems models. This is achieved using a population dynamics model between two groups characterised by a distinct food regime: omnivores and vegans. The greenhouse gas emissions of each food regime is used to estimate the city’s changing carbon foodprint as the food regimes shift by social contagion. Social contagion is identified as a catalyst for social tipping points, and emission pathways are explored with a variety of different contagion variables to test sensitivity towards a tipping point. The main finding is that the urban carbon foodprint can be reduced significantly with widespread adoption of veganism, but that the foodprint reaches a minimum at 1.97 tonnes CO2-equivalent per capita. This demonstrates the need to embed food demand in urban climate governance such as nudging towards plant-based food alternatives. Nudging is discussed as a lever of ecological importance to social contagion. Lastly, socio-ecological contagion is defined as the interactions between social contagion and damage done to ecological systems to measure peer-to-peer spread of environmental stewardship agendas, such as the journey to Veganville.
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 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 | 0.001 | 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 | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".