Bibliographic record
Abstract
Abstract Despite Merleau-Ponty’s well-known reservations about some aspects of Husserlian phenomenology, this chapter shows that the analyses of perceptual experiences carried out in the Phenomenology of Perception accord with Husserl’s on a fundamental respect: like for Husserl, Merleau-Ponty conceives of perception, illusions, and hallucinations both in intentional and normative terms. After having shown the role of the norms of concordance (Section 2.1) and optimality (Section 2.2) in Merleau-Ponty’s account of perceptions, the chapter provides a detailed analysis of his phenomenological conception of illusion (Section 2.3) and hallucination (Section 2.4) in turn, exposing how Merleau-Ponty defines both types of experiences in terms of the specific ways they break with the norms of regular perceptual experiencing. Throughout, the chapter insists more on the commonalities than on the differences between Husserl’s and Merleau-Ponty’s accounts of perceptual experience and demonstrates how both phenomenologists deal with the threat of scepticism (Section 2.5).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.009 |
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; both teacher heads agree on what is shown here.
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".