Bibliographic record
Abstract
In the era of scale up, global donor-driven HIV activities are transforming NGO work by demanding administrative, technical, and data-oriented activities. Drawing on interviews and participant observation in an NGO in the West Papuan city of Manokwari between 2011 and 2014, I attempt to understand why Indigenous Papuan NGO employees were steadily replaced by non-Indigenous migrant settlers, mainly of Javanese heritage, to deliver HIV services. I show that new rivalries, technical roles, performance targets and efficiency rhetoric intersected with existing racialization to produce a preference for Javanese employees, who were assumed to be more compliant and professional than their Papuan counterparts and to operate more easily within the technocratic regime imposed by donor expectations. I use the term technocratic racism to describe the way that global HIV rationalities intersect with ethnic stereotypes and gendered racial ideas to make possible certain HIV workers and not others. I contribute to anthropological literature on the delivery of HIV services by showing how a technocratic approach to HIV/AIDS intervention intersects with a settler-colonial context to gradually exclude Indigenous employees. Approaches that allow for relational, independent and flexible services would assist to decolonize HIV responses in West Papua.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".