Attempting intelligibility with the seemingly incomprehensible: Murambi, human remains and the Labour of Care
Bibliographic record
Abstract
In this article, I discuss the work carried out by the employees of the MurambiMemorial, a site commemorating Rwanda’s genocide, to highlight how theirwork-related responsibilities creates an opportunity for the memorial’s visitors tohave an intelligible encounter with the seemingly incomprehensible presence ofhuman remains. I introduce the concept of the Labour of Care, which provides abasis to think about how the work carried out by Murambi’s employees bestowsupon the human remains their interpretable qualities. From this basis, I examinehow this concept provides a means to think about human remains not simply asmaterial objects, but rather, better understood as subject-come-objects. By doingso, visitors can move beyond idealized notions of redemption and think abouthumanity’s unsettling capacity for violence.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.046 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.010 |
| 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".