Eugen Fink. De la surprise de l’apparaître : quand la surprise se perd dans la spéculation
Bibliographic record
Abstract
I have long been interested in Eugen Fink’s phenomenology and, more broadly, in his thought. This presentation gives me the opportunity to revisit his thinking in the light of an experience and a notion that the author has given very little attention to, but which to my mind fully characterises his mode of philosophising. The notion of “surprise” is such a projector for rereading. Since it is not used topically by Fink, we are entitled from the outset to ask whether it is, by virtue of the known distinction he himself proposes, an “operative concept”. Why surprise? There are two reasons: 1) A reason de facto: this concept has fuelled my thinking for more than a decade, and it has gradually emerged as a major starting point into the question of experience itself. I have even come to see it as a possible key to the renewal of phenomenology and even philosophy. 2) A reason de jure: Eugen Fink’s thought is not external to surprise, far from it. In my opinion, it turns out to be the native place of openness that is characteristic of his philosophy. For these two reasons, the aim of the following investigation is to explore how surprise is a fundamental vector of meaning for appearing as such, and how the appearing, despite its frequent and uninterrogated overlap with the event, gives itself in its phenomenal purity and freshness as surprise.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".