Bibliographic record
Abstract
In the time allotted to respond to this fascinating book, I will allow myself a few critical questions, coming from outside of Spinoza scholarship.I will assume familiarity with the book.I hope all of my comments, whether of the order of critique or clarification, will be taken as invitations to hear more about this exciting project. IHow does the "ethics of similitude" (Sharp 2011, 100, citing A. Matheron) relate to what has been called an "ethics of difference" (whose meaning I leave implied to capture a wider array of concerns)?Ethics here can be taken in at least two different senses: that of normative theory and, more common in continental philosophy, that of a social ontology.In the latter sense, ethics means, roughly, accounting for the way in which beings situate themselves in their place and time, make a home for themselves and for others, critically relating to inherited contexts in doing so.I take it Matheron and Sharp mean the social-ontological sense by the phrase 'ethics of similitude.' Key to the politics of naturalization is the move from a purely normative to an ontological register of doing political theory (10).In contemporary environmental ethics, for example, relations with the natural environment, or nonhuman entities therein, are considered merely under the aspect of whether normative terms developed within and for human moral and
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.044 |
| Scholarly communication | 0.012 | 0.023 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.017 | 0.026 |
| Insufficient payload (model declined to judge) | 0.009 | 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".