Bibliographic record
Abstract
I once heard it said that because of the sheer amount of time it takes, the person who begins writing a book is not the same person who finishes it. Nothing could be more true about my experience working on this project. The research began in the fall of 2006 when I enrolled in ‘Kant’s Ethics’, a PhD seminar co-taught by Sergio Tenenbaum and Arthur Ripstein at the University of Toronto. My final paper ended up becoming my first publication on Kant, ‘The Duty of Self-Knowledge’ (2009). After my area examination, I decided to pursue my dissertation project on Kant’s moral philosophy under the supervision of Paul Franks, which I defended in the summer of 2010. In the years that followed, I abandoned some of the views I upheld in my dissertation, went on to pursue different aspects of Kant’s ethics, and even ventured into the world of post-Kantian philosophy, writing a book on Fichte (2020) along the way. How the present book came into being is a bit of a mystery, even to me, but I can identify one set of encounters that made its existence possible.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.479 | 0.295 |
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".