Accounting for Violence: Marketing Memory in Latin America. KSENIJA BILBIJA and LEIGH A. PAYNE (eds.): Durham, NC: Duke University Press, 2011
Bibliographic record
Abstract
We seem to want to account for violence with the linear. Often, though, derivatives bend the curve. At Canada’s new Human Rights Museum, the Shoah is worth one gallery of twelve. So too is the slaughter of indigenous peoples. Genocides in Ukraine, Srebrenica, Rwanda, and Armenia, however, will all be crammed into a single gallery. Several groups balked at the math. The Ukrainian Canadian Congress wants a separate gallery for the Holodomor. Meanwhile, as construction costs (and perhaps the political costs of accounting for violence) soar, the Canadian government has pulled the plug on more funding. The outer building structure is complete. As experts argue, though, over the value in gallery units of one genocide or another, for now there is no money to finish the museum. The building shell becomes a metaphor.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".