The Conversion to sustainable agriculture: principles, processes, and practices
Bibliographic record
Abstract
The framework for conversion / Stephen R. Gliessman -- What do we know about the conversion process? Yields, economics, ecological processes, and social issues / Martha E. Rosemeyer -- The history of organic agriculture / Rachael J. Jamison and John H. Perkins -- Northern Midwest (U.S.) : farmers' views of the conversion process / Paul Porter, Lori Scott, and Steve Simmons -- Pacific Northwest (U.S.) : diverse movements toward sustainability amid a variety of challenges / Carol Miles ... [et al.] -- California (U.S.) : the conversion of strawberry production / Stephen R. Gliessman and Joji Muramoto -- Ontario, Canada : lessons in sustainability from organic farmers / E. Ann Clark and Jennifer Sumner -- Mexico : perspectives on organic production / Mar?a del Roc?o Romero Lima -- Mexico : traditional agriculture as a foundation for sustainability / Alba Gonz?lez J?come -- Cuba : a national-level experiment in conversion / Fernando Funes-Monzote -- The European Union: key roles for institutional support and economic factors / Gloria I. Guzm?n and Antonio M. Alonso -- Japan : finding opportunities in the current crisis / Joji Muramoto, Kazumasa Hidaka, and Takuya Mineta -- The Middle East : adapting food production to local biophysical realities / Avaz Koocheki -- Australia: farmers responding to the need for conversion / David Dumaresq and Saan Ecker -- Transforming the global food system / Stephen R. Gliessman.
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 | 0.011 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.007 | 0.067 |
| Scholarly communication | 0.021 | 0.012 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".