Bibliographic record
Abstract
The former Attorney General of New South Wales, Saxe Bannister, on whom this chapter focuses, travelled to the Cape Colony after being forced from his position in Australia. In the late 1820s, he argued for the need to apply British justice impartially in a frontier context, leaving an important record of colonial violence. He revealed abuses in colonial courts in two cases of settler violence against Khoekhoe people in the eastern Cape, despite putative British legal reform; highlighted Xhosa wrongs in the face of dispossession; and accused the colonial government of countenancing an illegal slave trade in San children. Khoekhoe people tried to use Bannister’s legal expertise to interact with the colonial government through petitions, while Xhosa chiefs funnelled statements of grievance through him, albeit to little immediate effect. I also suggest that Bannister thought of the Khoekhoe as analogous to the Haudenosaunee, in part because of their roles as military allies of the British, and may have shared political strategies with them. Bannister simultaneously believed that he, through his association with the trader Francis Farewell, had inherited a treaty with the Zulu leader Shaka giving the pair territory in Zululand. Despite being disappointed in his effort to monetize this ‘treaty’, Bannister later vigorously promoted the creation of the British colony of Natal. The chapter explores paradoxes of imperial liberalism and its difficult relationship to anti-colonial resistance.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.200 | 0.039 |
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".