Bibliographic record
Abstract
ABSTRACT This paper follows the implementation of landmine detection rats in Cambodia. Over the course of my ethnographic fieldwork with the team for the landmine detection rat technique training in Cambodia, I saw that the way the human landmine detectors (a.k.a. deminers) learned to love the rats also changed how they also learned to live and work with each other. Deminers themselves were usually former soldiers, many of whom had fought on opposite sides during Cambodia's civil wars. Their labor in the minefield was inherently aspirational, and rats were employed in this future imaginary. They worked with former soldiers who, as Theravada Buddhists, would sometimes use the religious word “metta” for the love they felt for their rats, which they defined as “pity‐love.” Metta is a love, I was told, “that can make a cruel person kind.” This expression had a particular valence for deminers who had to work with former combatants, where the stigma of past violence made it uncomfortable to directly address such former enmities. Loving the rats sublimated the minefield's potential violence into potential pity‐love for and with colleagues, mediating a violent past to work together toward a previously unimaginable future: a postwar ecology.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".