Not all BMPs are Created Equal: No Regret, Neutral, Sacrifice and Dead End BMPs
Bibliographic record
Abstract
Introduction: The science of anthropogenic global warming is well established. Burning 100 M barrels of oil equivalent does do harm, yet in the USA Mid-West as well as in Alberta 90% of farmers sampled either did not believe in AGW or thought it is a natural process. Purpose: to provide an alternative view of twelve BMPs and their importance and adoption by leading innovative global warming wary farmers operating in the Northern Great Plains. Findings: Analysis of the exploratory in-depth qualitative narrative-based research work conducted during my PhD thesis has resulted in development of Rourke’s General Farm Practice Change Theory, a Net Positive farm Framework and a Global warming Mitigation credit framework. It went further to develop a BERT/E BMP adoption scoring system, where B= Beliefs, E= Economics, R= Regulatory environment, T= scalable local pragmatic technology and the second E, the denominator is the Energy of the farmer physically and mentally to make the change(s). Practical Implications: To be widely adopted BMPs must have a high BERT/E score and can be grouped into 4 categories, No Regret, Neutral, Sacrifice and Dead End BMPs. The study found while farmers may believe in a wide variety of BMPs, it is only the very few which are No Regret BMPs that are widely adopted and are needed to become Net Positive. Two of the 12 farm participants were Net Positive. These farms also had high Sustainable Farm Indices, SFI, which balances farm profitability, farm output with farm emissions—a Triple Win.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".