Disentangling war and disease in post-conflict Colombia beyond technoscientific peacemaking
Bibliographic record
Abstract
In November 2016, the Colombian government and the Revolutionary Armed Forces of Colombia (FARC) signed a peace agreement to end a 52-year war. In the context of the peace deal implementation, I ethnographically traced entanglements of biomedicine, public health and the armed conflict across shifting temporalities and realities of war and peace. Through an exploration of past, present and future (dis)entanglements of war and leishmaniasis – a vector-borne disease known by many in Colombia as “the subversive disease” or the “guerrilla disease” – this article traces a discourse that frames health problems, like leishmaniasis, only as scientific or technological challenges. Drawing on STS critiques of future-oriented timelines in technoscience and the concept of pharmaceuticalization, I argue that the expectations embedded in technoscientific innovation problematically limit the possibilities of disentangling leishmaniasis and war in post-conflict Colombia. In contrast, ethnographically exploring how health policies and biomedicine have nurtured violence and exclusion helps us destabilize the warfare-loaded meaning and experience of leishmaniasis. This approach enables us to move beyond imaginaries of technoscientific peacemaking, which I define as the excessive trust endowed to technoscience to (re)build a peaceful future, especially when we are faced with failures in understanding the involvement of technoscience in the production and perpetuation of violence.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.006 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".