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Hunting, Wildlife, and Imperialism in Southern Africa

2007· book-chapter· en· W3102159716 on OpenAlexaboutno aff
William Beinart, Lotte Hughes

Bibliographic record

VenueOxford University Press eBooks · 2007
Typebook-chapter
Languageen
FieldSocial Sciences
TopicArchaeology and Rock Art Studies
Canadian institutionsnot available
Fundersnot available
KeywordsGeographyWildlifeIndigenousArchaeologyEcologyBiology

Abstract

fetched live from OpenAlex

Imperial networks in northern North America flowed initially along the waterways that gave access to the trade-associated hunting, trapping, timber extraction, and related activities. Hunting was essential to many indigenous societies, and required relatively little investment for the first wave of traders and settlers. It generated valuable resources in a number of colonized zones. But hunting frontiers in the British Empire differed. In part, the reasons were environmental. The assemblage of species in North America and Africa were possibly more similar 15,000 years ago ‘when the American West looked much as [the] Serengeti plains do today’. Large mammals including mammoths, big cats, and wild horses roamed the northern hemisphere prairies. Climate change, combined with the impact of rapid human migration through the Americas 10–12,000 years ago, resulted in many extinctions so that the wildlife of the two areas had become distinctive by the onset of European colonization. This opened up divergent opportunities for consumption and trade. Southern Africa was a frontier of heat rather than cold. There were no animals with the thick glossy fur favoured by Europeans for outer garments or for felt. Southern Africa’s most prized hunted commodity—aside from meat—was equally unpredictable. While mammoths had been exterminated in North America, an elephant species with large tusks survived into the modern era in Africa. Environmental factors also shaped the technology of hunting and carriage. Southern Africa lacked navigable rivers and lakes; Canada’s abundance of water was matched by South Africa’s dearth. Although the spread of firearms and horses was common to both regions, South Africa’s transport sinews were dusty, rutted ox-wagon tracks across the veld rather than the cool, wooded lakes and streams along which canoes could be paddled. In part, differences resulted from the chance value of particular animal products. Southern Africa was home to an extraordinary range of large mammals. The richness of wildlife can be judged by the variety of predators at the top of the food chain—lions, leopards, cheetahs, caracals, hyenas, wild dogs, jackals, as well as smaller cats. The antelope population was unparalleled in the diversity of its species. But variety did not in itself translate into value.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.991
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.037
GPT teacher head0.247
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations1
Published2007
Admission routes1
Has abstractyes

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