Bibliographic record
Abstract
As I reread and reflected on Sex, Love, and Gender, I found myself asking what the bar for success for the goals of the book is, at least insofar as these goals concern its non-Kantian audience (I know there are a rich array of contributions to the inside-baseball of Kant scholarship here that just go well over my head). Where does this leave those of us whowork on sex, love, and gender within feminist, anti-oppressive, and inclusive traditions, inour relationship to Kant? Asking this question highlights the subtlety and significance of whatVarden accomplishes. She is not aiming at anything so heavy-handed as conversion, I think (orat least, I hope not), but rather, at opening up new kinds of conversation. I will no longer helpmyself to straw-Kantians in discussions of the emotional, relational, and physical dimensionsof our personhood, and that I must move Kant from target to unlikely ally in some of thetopics I take closest to heart. I still think that the strength of Kantian theory (its complex andcomprehensive systematicity) is also very much its weakness (it often feels possible and easierjust to get from A to B without all this machinery, when discussing – for example, the value ofphysical love, the importance of respect, or the significance of abortion rights) but I now findmyself to compelled to double check that I am not missing something, when I skip past it. Thank you to Helga for such a provocative, thoughtful, brilliant and personal book. Iam grateful for having had the opportunity to read it, and a bit surprised to realize how muchI will bring Kant into future conversations on these topics.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".