Action Plan for the International Year of Rangelands and Pastoralists (IYRP): The Case for the United States, Canada, and Mexico
Bibliographic record
Abstract
The GAP analysis (A Case of Benign Neglect: Knowledge gaps about sustainability in rangelands and pastoralism) points to several gaps that are relevant to the US, Canada and Mexico. North American rangelands span the ecological continuum of polar to hot deserts and arid to humid climates that exhibit highly variable ecological and forage production potential across time and space. Although there is a great deal of rangeland research, extension, and inventory capacity in all three countries, a weak link is the dissemination of information to North American pastoralists (conventionally referred to as ranchers or producers). Although the extension system in the US and Canada are similar, there are distinct differences. Public lands in the US are managed at the national level by federal agencies (e.g., Bureau of Land Management and Forest Service) while private land management assistance is provided by the Natural Resource Conservation Service. In Canada, Crown land is managed by departments within each province and there is no national extension service. In Mexico, the majority of the lands are managed by local communities or ejidos, 15% are privately owned and managed and the remaining 5% is government owned. The three countries support national research organizations and have a well-developed system of colleges and universities that have range management or related disciplines containing staff that specialize in teaching and/or research (and cooperative extension at land grant universities within the US). All three countries must attempt to bridge gaps between an urban industrial society that is increasingly disconnected from extensive agricultural production on rangelands. Promoting ecological goods and services provided by rangelands is a relatively new paradigm for US, Canadian and Mexican research and extension. During the IYRP, the focus in the US, Canada and Mexico is likely to be in 2 directions; providing North American pastoralists/ranchers with the social license to continue to ranch or farm while educating the massive urban population about the sustainability, multiple uses, and benefits of ecological services produced on rangelands and native grasslands.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".