Discourses of Development and Practices of Punishment
Bibliographic record
Abstract
Abstract From 1952 to 1960, the landscape of Kenya was drastically changed by the British through the widespread implementation of detention camps, prisons, and strategic villages in response to the ‘Mau Mau’ insurgency. Of the 80,000 detained, approximately 8,000 were female, and of the 1.2 million Kenyans forcibly resettled, the majority were female. While many colonial states targeted female insurgents, these gendered geographies of coercion were particularly pronounced in Kenya and were shaped by racialized notions of respectable and deviant femininity. Within these sites, the British introduced development and ‘rehabilitation’ schemes to encourage women and girls to embrace domestic roles in support of colonial visions of social order. Colonial officials sought to carefully code and categorize their counter-insurgency efforts, presenting the strategic villages and the rehabilitation programme in the camps as part of their wider ‘civilizing mission’ while insisting that any violence in these sites was incidental or aberrant. This framing not only ignored the reality of systematic abuses in these spaces, but also narrowly defined violence as solely a physical phenomenon. Instead, this chapter argues that violence, in its multiple dimensions, was not simply an outcome of counter-insurgency development and ‘rehabilitation’ schemes, but was rather intrinsic to them.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.050 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| 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".