A new zoogeography of domestication and agricultural planning in Southern Ghana
Bibliographic record
Abstract
Animal behaviour is vital for livestock choices, but is less researched in West Africa than economic considerations. An animal geography framework is applied to the socio‐economic context of livestock behaviour in coastal Ghana, assessing the shared ‘actant’ behaviour of people and animals, and the contribution of such a study to animal geography and agricultural knowledge. Data were gathered on cattle, sheep and goat behaviour and the impact of these on human livelihoods, perceptions and the socio‐environmental context. Animal behaviour was more important in the choice of livestock species, but economic considerations were more important in the decision to acquire animals. Goats had more incidents with people in village centres than sheep and cattle. Cattle had more incidents in farmland and grassland than goats and sheep. Women and young people were more affected by livestock behaviour. These findings increase the understanding of livestock zoogeography and livelihood decisionmaking, and contribute to animal geography by documenting the relevance of individualised gender‐ and age‐based human behaviour, as well as intra‐ and inter‐species animal behaviour to a shared actancy perspective, and a more dynamic zoogeography.
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 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".