On the Delineation of Choice and Decision in Benjamin's "Goethe's Elective Affinities"
Bibliographic record
Abstract
This paper will attempt to approach, through a reading of Benjamin's essay Goethe's elective affinities, the question of the relation between the possibility of the instant of decision or the madness of decision 1 and its relation to what might be called a law of calculation or tragic fate. 2 The concept of the irreducibility of decision, as Derrida has argued, opens immediately on a major problematic or aporia: if the absolute singularity of decision, a singularity which cuts itself off from all rational calculation, is what determines any decision worthy of the name, then how does one know that there has been a decision, that a certain calculation has been broken or interrupted? How does one know, in other words, that a decision is truly a decision and not a mere repetition of an already decided fate, of a decision that has already decided? Heidegger, in the third volume of the Nietzsche lectures, poses this problem of wresting or delimiting the genuine decision from out of its relation to its counterfeit thus:
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.062 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| 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".