Presenting the (Dictatorial) Past in Contemporary Argentina
Bibliographic record
Abstract
Drawing upon Isabelle Stengers’ notion of an ‘ecology of practices’ this article explores some of the divergent ways in which truths about the violence of Argentina’s last dictatorship period emerge in different forums. We consider how these forums deploy ‘arts of dramatization’, which is to say, the ways they stage questions about the violence of the last dictatorship period in order to propose, explore, confirm and sometimes refute ‘candidates for truth’. Following Stengers’ provocations, we argue that the various modes of staging the past conjure up its violence in distinct ways, placing different constraints on how it can appear, using different material apparatus and probing it according to different values under different obligations. Based on interviews and observational research with key personnel – including lawyers, artists, forensic anthropologists and psychologists – we suggest that while each of the forums within this ecology is concerned with truth, how and what emerges as truth necessarily differs. What counts as evidence, what is understood as ‘successful’, what is dismissed as irrelevant are all dependent upon the concerns of the forum, such that truths about Argentina’s dictatorship are not only ‘situated’ but also necessarily ‘partial’ forms of world-making. In an attempt to propose a shift from over-determined and usually binary lines of debate, we suggest that these truths exist within an ‘ecology of practices’, to use Stengers’ term, insofar as these forums are not closed off from each other, but are becoming a web of often highly interdependent connections, wherein personnel, practices, audiences and resultant ‘truths’ travel.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".