Who's afraid of Allan Savory? Scientometric polarization on Holistic Management as competing understandings
Bibliographic record
Abstract
Abstract How to graze livestock sustainably is an important and complex question. The debate between rotational and continuous grazing has been ongoing since the 1950s, yet evidence is perennially mixed. We used scientometrics to understand the structure of science on Holistic Management (HM), the most contentious of these adaptive practices. We used papers in Web of Science since 1980 citing the work of HM's ‘father’, Allan Savory, as a way of delineating a field that is otherwise chaotic with terminology. Results show an increasingly diverse use of Savory's work geographically and in terms of subject areas. Taking a positive position on HM seems most likely for those doing farm-scale (rather than experimental) work in dry climates. Bibliographic factions align with the various disciplines working on grazing research and also their expressed opinion on HM practices. Factions represent disciplinary strength, suggesting barriers for integrative work but also the need for the resolution of competing understandings in specific contexts with diverse participants to inform grazing decisions.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | BibliometricsScience and technology studies Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.092 | 0.369 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.069 | 0.130 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.020 | 0.012 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".