Affects, Images and Childlike Perception: Self-Other Difference in Merleau-Ponty’s Sorbonne Lectures
Bibliographic record
Abstract
I begin by reviewing recent research by Merleau-Ponty scholars opposing aspects of the critique of Merleau-Ponty made by Meltzoff and colleagues based on their studies of neonate imitation. I conclude the need for reopening the case for infant self-other indistinction, starting with a re-examination of Merleau-Ponty’s notion of indistinction in the Sorbonne lectures, and attending especially to the role of affect and to the non-exclusivity of self-other distinction and indistinction. In undertaking that study, I discover the importance of understanding self-other distinction and indistinction in terms of their affective significance. For Merleau-Ponty, self-other indistinction is a virtual or imaginary participation in others’ orientations that he defines as an affective phenomenon. Further, Merleau-Ponty’s account of the advent of the body proper—the aspect of the body image that circumscribes the body as a distinct and private space—theorizes it as an affective innovation. Rather than being a fact of which we at first are ignorant and gradually grow to recognize, distinction from others in the sense that is important to Merleau-Ponty is a situation that must be cultivated and maintained through the negotiation of affective intimacy. Understanding indistinction and distinction in terms of the affective forces that sustain them explains how it is possible for them to coexist.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".