L’impact de la fragmentation des mémoires collectives nationales sur la politique étrangère : le cas de la France
Bibliographic record
Abstract
Comment les changements dans les conditions de la gouvernance des mémoires collectives nationales influencent-ils la politique étrangère ? Le recours à des représentations du passé partagées à un niveau intersubjectif constitue une ressource pour la prise de décision et pour la justification de la politique étrangère. Dans cette optique, cet article identifie cinq facteurs qui amenuisent la capacité des gouvernements de régir les mémoires nationales et en explore deux conséquences principales. Tout d’abord, l’utilité justificatrice du recours à l’histoire est de plus en plus limitée. Ensuite, la disparition d’une mémoire collective consensuelle peut conduire à l’émergence du « présentisme » comme mode de temporalité dominant dans les processus de décision. Cette réflexion est illustrée par une analyse empirique du cas de la France, pays dont la politique étrangère se distingue par une forte tradition du recours à l’histoire nationale.
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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 | 0.004 | 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 | 0.010 | 0.013 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".