Bibliographic record
Abstract
The converging climate, biodiversity, public health and nutrition emergencies highlight the need for more regenerative food systems. Despite the recognition that regenerative food systems enhance resilience, resource efficiency, and equity, they continue to be dwarfed by extractive industrial approaches. One factor that is holding back regenerative food systems is their lack of access to financial capital. In response to this financing gap, social financiers have turned their attention to regenerative food systems. To date, the scholarship exploring the role of social financing in supporting regenerative food systems is limited. Yet, this is an important area of study for understanding the tools that could support pathways towards greater social and ecological resilience in our food systems. This paper develops propositions on the links between social financing and regenerative food systems, with qualitative insights used as illustrations. Six semi-structured interviews were conducted with key stakeholders related to social finance and regenerative food systems in the United States. Additionally, this paper draws on information gathered through presentations from the Regenerative Food System Investment (RSFI) forum. The analysis identified five observations that enrich the social finance and food systems literatures: (1) those who get funded are not necessarily the best placed to advance the goals of regenerative agriculture; (2) tensions exist between the way that scholars and practitioners view social finance; (3) impact metrics are in flux and must be approached thoughtfully; (4) the middle of the food value chain remains severely underfunded; (5) early steps are being taken to maintain diversity that is core to the resilience of regenerative food systems. Topics for further research in this emerging area are identified in the conclusion.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".