Bibliographic record
Abstract
How to tell the story of the Haitian earthquake?Ever since 4:53 p.m. on 12 January 2010, this is the question many of Haiti's writers have been asking.In the face of such a catastrophe with widespread death, destruction and suffering, what can writers do?In terms of aftershocks, how have the thematics and representational form of Haitian writing developed in the wake of this seismic event?This article examines responses in Haitian writing to the earthquake through the prisms of a premonitory play entitled Melovivi ou Le Piège ( The Trap) by Frankétienne, and of chronicles and essays written by Dany Laferrière, Yanick Lahens and Gina Athena Ulysse, who, this article argues, react to threats to archives and material culture by creating a unique 'aura' of archival documents in the very form of their books themselves 1 . 2In terms of archival matter, my reading is based upon the literary manuscripts of Frankétienne's play Le Piège, and Yanick Lahens's Failles (Fault Lines) and Guillaume et Nathalie 2 .For the analysis of the actual manuscripts, this article exploits methodologies for studying literature in statu nascendi from genetic criticism-a predominantly French phenomenon, which has rarely been considered in the same context as postcolonial studies 3 .Genetic criticism's methodology for studying the dynamics of stages of Writing the Haitian Earthquake and Creating Archives Continents manuscrits,
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.023 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".