Bibliographic record
Abstract
In winter 2013, at the request of my students, I taught a seminar on Merleau-Ponty.Up to that point, I had not delved seriously into Merleau-Ponty's thought and it is fair to say that reading -as well as teaching -Merleau-Ponty had a significant impact on my own thinking.My biggest debt of gratitude then is to my students, not only those who made their first foray into Merleau-Ponty's work with me in 2013, but also those who studied Merleau-Ponty with me in winter 2019.My understanding of Merleau-Ponty and my ability to explain his most complex ideas is indebted to our conversations.I am also grateful to all those who participated in our Nancy reading group in 2019 -Mackenzie, Júlia, Felix, Markéta, Jay -for reviving my enthusiasm for Nancy.Along the way, there were many conferences and informal conversations with colleagues and friends, for which I am also grateful.Some parts of the following study have also been published in various forms.A much shorter version of Part I appeared as 'Corps propre or corpus corporum: Unity and Dislocation in the Theories of Embodiment of Merleau-Ponty and Jean-Luc Nancy' in Chiasmi International 18 (2016): 353-70.An earlier version of Chapter 8 appeared as 'Flesh and Écart in Merleau-Ponty and Nancy', in Irving Goh (ed.), Nancy Among the Philosophers (New York: Fordham University Press, 2022).
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 | 0.008 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.425 | 0.337 |
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".