Bibliographic record
Abstract
To understand Hermann Cohen's systematic philosophy in conjunction with his writings on Judaism has been an interest of mine for many years, beginning with the work that went into a chapter of my first book (Exemplarity and Chosenness: Rosenzweig and Derrida on the Nation of Philosophy [2008]) that looked at Cohen as a prime influence on Franz Rosenzweig's Star of Redemption.My idea to work on this topic in a sustained fashion began to take shape thanks to a most stimulating year I spent as a postdoctoral fellow with the UCLA Mellon Sawyer Seminar on "The Ethics of the Neighbor," directed by Kenneth Reinhard.Subsequent stages of the project were enabled by a Standard Research Grant from Canada's Social Sciences and Humanities Research Council (SSHRC) and a fellowship from the Alexander von Humboldt Foundation.That fellowship allowed me to spend eighteen months at the Zentrum für Literatur-und Kulturforschung (ZfL, Center for Literary and Cultural Research) in Berlin, leading to a summer stay funded by one of the ZfL's own fellowships.I am most grateful to Kenneth Reinhard and Martin Treml, my hosts at UCLA and the ZfL, respectively, as well as to other colleagues and staff at both institutions, for providing such nourishing environments in which my thinking toward this book began to develop.Martin's and Ken's intellectual partnership allowed for many fruitful exchanges during my stays, and enabled us to collaborate on colloquia and workshops.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.443 | 0.266 |
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".