Conversion to Organic Farming and Social Justice: A Socio-Ecological Approach
Bibliographic record
Abstract
Conversion to organic farming is one solution to the costs which globalization has imposed on the human and physical environment. In these systems, agro-ecological, socio-cultural, economic and institutional factors are integrated, affecting the daily life of the organic producers and their social network, To understand this reality this study used a systemic and holistic socio-ecological approach as well as a network framework, because it presumes that farmers, researchers, certifying bodies, firms, government authorities and non-government organizations (NGOs) are all involved in the complex web of material and non-material relationships, that affect changes and decisions This analysis seeks to understand problems, issues, trade-offs, objectives and perceived needs for making appropriate decisions, which could connect sustainability to human rights, equity, responsibility and social justice. Preliminary results based on secondary data and on the life story interview with a sub-sample of producers showed that the conversion process to organic farming depends not only on economic factors, but also on socio-cultural and institutional (both public and private) parameters. The implementation of organic farming has challenges and it is associated with a change of values, based on a philosophy and the life style of the actors involved in the organic system, with all their complexities and interest. The model of development imperative in Canadian economy isn’t fair in terms of social justice and effective sustainability, because it is governed by neo-liberal economic forces that sacrifice the social dimension and don’t humanize the dynamics of the complex system of interest of the organic production.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.010 | 0.032 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".