Modern pastoralism and conservation: old problems, new challenges
Bibliographic record
Abstract
1. Contemporary Pastoralism: Old Problems, New Challenges. Anatoly M. Khazanov 2. Cattle Breeding, Complexity and Mobility in a Structurally Unpredictable Environment: the WoDaaBe Herders of Niger. Saverio Kratli 3. Disentangling 'Forced Displacement' from Pastoral Mobility: Recovery and Reconstruction in the Sahel and in South Sudan. Salem Mezhoud and Clare Oxby 4. Pastoralists at Crossroads: Community Resource Governance in the Context of a Transitioning Rangelands Tenure System. Stephen S. Moiko 5. Booking and Grabbing Land: Strategies of Appropriation in Loita Maasailand, Kenya. Angela Kronenburg Garcia 6. Adapting to Biodiversity Conservation: The Mobile Pastoral Harasiis Tribe of Oman. Dawn Chatty 7. Tradition and Transition in the Mongolian Pastoral Environment. Troy Sternberg 8. Theorising Ecological Migration. Emily T. Yeh. 9. Can Ecological Migration Policy in the Tibetan Plateau Region Achieve both Conservation Goals and Human Development Goals? A Review of the Canadian Experience of Relocation and SettlemenT. J. Marc Foggin and Gongbu Zhaxi
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".