The Sustainable Agriculture Initiative Platform: the first 10 years
Bibliographic record
Abstract
This teaching case, appropriate for senior undergraduate and MBA students as well as for mid-career managers, challenges students to appreciate different facets of agribusiness sustainability, and to reflect on the potential consequences on all players in the global supply chain, from farmers to retailers, of the different methodological and philosophical approaches, definitions, and practices that are developed and adopted by the different constituencies, from independent industry organizations, to food processors and retailers. The case ‘story’ is structured around a strategic decision on what further development activity should be adopted by the Sustainable Agriculture Initiative (SAI) Platform. SAI has just turned 10, its activities have proven quite successful, and the Platform is considering the next strategic move. The case frames the challenges facing SAI in a broader context, illustrating what SAI as well as an independent organization, the world largest food processor and largest retailer are doing about defining and measuring sustainability. The broad question is: what is the best way forward for SAI and what are the key implications of what SAI may decide, given the ongoing development of what is the reasonably new sector of defining, measuring and promoting sustainability in agribusiness? The reading materials provide context and methodological underpinnings to the case, which is designed to be discussed over two classes, ideally on separate days. The coach should withhold Part Two until the discussion of Part One has been completed. The Teaching Note, available to verified instructors considering the use of the case, provides additional suggestions on the classroom use of this material.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.027 | 0.010 |
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".