Agrarian futures and normative practices: A case study of small-scale ecological farming
Bibliographic record
Abstract
This research paper investigates normative approaches to socio-ecological change through a case study of small-scale ecological farming. This paper makes the claim that a variety of normative responses and practices responding to contemporary socio-ecological crises face barriers that are internally related to the contradictions of development and market-based approaches to change. This paper will show that there are normative responses to future formations of socio-ecological relationships that arise from ecological modernism, that emphasizes technological change in agriculture such as through ‘precision agriculture’, and alternative food networks and food sovereignty, that is critical of conventional agrarian development. This paper examines these normatively based framings about agrarian futures and development, with a particular focus on practitioners in non-conventional and alternative food networks. Through in-depth semi-structured interviews with farmers, market gardeners, and other participants with knowledge and experience of the local food provisioning context in southern Manitoba, and through participant-led ‘farm walks’, this research sought to observe, examine, and contextualize the experiences and perceptions of select operators of small-scale ecological farms, and the barriers they face in practicing ecological farming.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".