"'We Have to Keep Moving': Transnational Witnessing in Dany Laferrière's The World is Moving Around Me"
Bibliographic record
Abstract
Dany Laferriere has never claimed to represent Haitian identity, either as a person or in his texts. Constantly moving between Haiti, Montreal, Paris, and other places in the world, Laferriere explores the idea of identity as a shifting transnational category by using strategies such as playing with his role and position as a writer both within and outside his fictional worlds. The earthquake that struck Haiti on January 12, 2010, gave his play with the authorial identity an acute seriousness, as he quickly assumed the role as an eye-witness to the disaster in international media. What happens then when someone who is notoriously known for refuting any national and cultural identity suddenly becomes the voice of the inside? Drawing on Blanchot's theories of disaster writing, on Mark D. Anderson's study of disaster and national identity and on Mads Rosendahl Thomssen's notion of world theme, the aim of this essay is to analyse how Laferriere uses his transnational experience of global movement and of being at once inside Haiti, and thereby reconfigures the notion of disaster writing. I will show how the very act of witnessing transfolds in a transnational setting which has an impact on writing itself, and I will problematise the ways in which Laferriere's writing operates as to avoid turning the disaster into an event by creating a narrative that paradoxically builds a sense of continuity through fragmentation.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.043 | 0.029 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 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".