Bibliographic record
Abstract
When Paul Ricoeur’s Time and Narrative was published, it was hailed as a major contribution to the philosophy of history. In the four decades since, Ricoeur’s brand of narrativism has fallen out of favour, and many of the claims he makes about historical narrative have been sharply criticized. Nevertheless, I argue that Ricoeur’s narrativism remains a powerful resource for philosophical reflections on the study of history. After reconstructing Ricoeur’s narrativism and placing it in its larger context, I discuss two sorts of criticism that have been directed at it: one made by literary theorists such as Monika Fludernik and another made by philosophers such as Jouni-Matti Kuukkanen. I defend Ricoeur from both sorts of criticism, arguing that when his approach is compared to its competitors, it fares remarkably well. I conclude with some suggestions about how philosophers of history might build on Ricoeur’s legacy today.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 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".