Bibliographic record
Abstract
Cattle are paramount to lives, livelihoods and landscapes in Botswana. Human-cattle relations emerge and evolve through historically-situated social relations of power based on gender, ethnicity, and class. Our paper explores intersectional human-cattle relations in Botswana within the contemporary period of enhanced commercialization. Specifically, with data from participant observation and semi-structured interviews with women cattle owners in Ghanzi District, Botswana, we investigate how women across a range of ethnicities become-with cattle and how cattle are becoming-with women cattle owners, directly or mediated through hired labour and/or technology. By operationalizing Haraway’s multispecies ‘becoming-with’ through intersectionality theory we articulate the nuanced ways in which individuals or social groups of two distinct species (here humans and cattle) become who they are. We show that whereas gender and ethnicity dynamics place women as engaging directly with cattle, engaging indirectly with cattle or becoming-without cattle, class most visibly shape the way that cattle become-with women cattle owners and other humans. We offer a novel illustration of an intersectional becoming-with, highlighting human-animal relations in the context of agriculture and socio-economic change in the Global South.
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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 | 0.001 | 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 | 0.007 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".