Best versus beneficial MP discourses: The significance of a change in discourse managing agricultural water quality in Canada
Bibliographic record
Abstract
Best Management Practices and Beneficial Management Practices ("MP") are two ideational elements, that have emerged in water quality literature. This research explores the questions of where has the lesser utilized term 'Beneficial' emerged and why? To answer these questions, articles obtained from the Web of Science by searching keywords "Best MP"/"Beneficial MP" and "Water" are analyzed using bibliometric techniques through VOS Viewer including time trend of publications, and indicators including keywords, disciplines, institutions, geographies, influential authors and journals, and key funders of these two terms. This paper also employs document analysis and semi-structured interviews with key policy stakeholders. Beneficial Management Practices emerged in Canada (although the term was used in a few instances elsewhere) in Canadian government policy documents starting in 2003. The term 'Beneficial' refers to a lesser standard than "Best" and is exclusive to agricultural practices in risk and environmental farm policy; "Best Management Practices" refer to a wider set of practices (in other sectors like mining) utilized by a more substantive and diverse set of institutions (predominately American and significantly populated by universities). Explanations for the emergence of the term include that it is more 'honest,' it allows for more choices and trade-offs, it reflects the strong economic driver of agriculture, and several interviewees referred to it better reflecting the uncertainty of science. While the strength of the agricultural sector in influencing the 'Beneficial' discourse is not surprising, the failure to measure the improvement to the water quality of Beneficial Management Programs and measure policy's effectiveness is noteworthy.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.016 |
| Science and technology studies | 0.034 | 0.031 |
| Scholarly communication | 0.026 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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".