Bibliographic record
Abstract
Abstract Pastoralists depend for their livelihood on raising livestock on natural pasture. Livestock may be selected for meat, milk, wool, traction, carriage, or riding, or a combination. Pastoralists rarely rely solely on their livestock; they may also engage in hunting, fishing, cultivation, commerce, predatory raiding, and extortion. Some pastoral peoples are nomadic and others are sedentary, while yet others are partially mobile. Economically, some pastoralists are subsistence oriented, others are market oriented, and others combining the two. Politically, some pastoralists are independent or quasi-independent tribes, others, largely under the control of states, are peasants, while yet others are citizens engaged in commercial production in a modern state. All pastoralists have to address a common set of issues: gaining and taking possession of livestock, including good breeding stock. Ownership of livestock may consist of individual, group, or distributed rights, managing the livestock through husbandry and herding. Husbandry is selecting animals for breeding and maintenance. Herding is ensuring that the livestock gains access to adequate pasture and water. Pasture access can be gained through territorial ownership and control, purchase, rent, and patronage. Security must be provided for the livestock through active human oversight or restriction by means of fences or other barriers. Manpower is provided by kin relations, exchange of labor, barter, monetary payment, or some combination of these. Prominent pastoral peoples are sheep, goat, and camel herders in the arid band running from North Africa through the Middle East and northwest India, the cattle and small stock herders of Africa south of the Sahara, reindeer herders of the sub-Arctic northern Eurasia, the camelid herders of the Andes, and the ranchers of North and South America.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.171 | 0.032 |
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".