Combat-related killings and democratic accountability: towards an understanding of the cultural capacities to deal with matters of war (conference essay)
Bibliographic record
Abstract
This report was written by the organizers of the workshop "Accounting for combat-related killings", which took place at the Goethe University Frankfurt in July 2014. Scholars from Israel, the United Kingdom, the United States, Canada, and Germany came together to present and discuss case studies on the discourse practices involved in accounting for combat-related killings in different national and transnational contexts. Intending to reflect on the methodological skills needed to analyze newly available process data, the workshop brought together scholars using different methodological approaches (here mainly ethnomethodology and critical discourse analysis). In regard to the global trend towards increasing numbers of so called permanent, asymmetric, small, and permanent wars, the report turns to concepts, methods, and empirical findings that foster understandings of the difficulties war generates at social, cultural and political levels as well as the manner in which these predicaments are negotiated, denied, or deflected. The report summarizes the workshop by presenting the papers in a specific order, beginning with accounting in combat, followed by tribunals of accounting, and finally the sedimentation of accounting in cultural representations. (author's abstract)
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.026 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| 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".