<i>Galadio</i>de Didier Daeninckx et la question de l'identité nationale
Bibliographic record
Abstract
In 2009, the French government launched a national debate to define French national identity. The people, as well as experts and intellectuals, responded to this initiative in many ways. The discussions often led to determining who belonged to the national community and who did not. Hence, descendants of immigrants often became a target in the debate. Didier Daeninckx, a well-known writer who often intervenes in the public sphere, published a short novel entitled Galadio (2010) as a response to the debate imposed by the government. It tells the story of a young man in 1930s Germany whose father, a tirailleur sénégalais, met his mother, a German, during the 1920s when French colonial troops were stationed in the Ruhr region to pressure Germany to honour the Treaty of Versailles. This analysis seeks to assess what this literary piece adds to the debate by focusing on its complex conception of identity. The form of the historical novel allows Daeninckx to remind us of the participation of the African colonies in both WWI and WWII. The main character's identity is also multidimensional, which is essential to the novel's effort to avoid simple definitions of national identity unambiguously tied to nationality. Finally, following a protagonist who has strong ties to Germany, colonial West Africa and France allows for a geographical and temporal displacement necessary for the allegory mechanism to function and for its meaningfulness to become obvious in 2010.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".