An Indicator Framework for Resilient Agroecosystems in Ontario, Canada
Bibliographic record
Abstract
Southeastern Ontario presents a unique agricultural landscape in which both alternative and conventional farms operate. Each category of farm experiences a wide range of environmental and socioeconomic shocks and stresses that threaten each farm’s longevity, raising questions about what constitutes resilient agriculture. Yet in Ontario there presently exists no unifying framework for building agricultural resilience. In response, this project mobilizes an analytical framework based on resilience indicators to create a qualitative method for assessing farm sustainability. Implementing this framework in the study area of Inverary, South Frontenac, semistructured interviews based on resilience indicators were conducted with both alternative and conventional farmers. The interviews enabled us to identify primary issues experienced at the farm level and account for the ways that their operations are structured to mitigate risks and achieve valued outcomes. Our research found that diverse farmers are facing overlapping threats but are prioritizing different responses to those threats by relying on distinct indicators of resilience. Key insights after using the indicator framework are summarized to create a deeper understanding of agricultural resilience in the province.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.018 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".