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Record W4389473756 · doi:10.1007/s11097-023-09951-w

Giving thickness to the minimal self: coenesthetic depth and the materiality of consciousness

2023· article· en· W4389473756 on OpenAlexaff
Istvàn Fazakas, Mathilde Bois, Tudi Gozé

Bibliographic record

VenuePhenomenology and the Cognitive Sciences · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicMental Health and Psychiatry
Canadian institutionsConcordia University
FundersFonds De La Recherche Scientifique - FNRSAgence Nationale de la RechercheBergische Universität Wuppertal
KeywordsMateriality (auditing)Phenomenology (philosophy)ExistentialismConsciousnessFeelingSelfAestheticsExperiential learningTranscendental numberPsychologyEpistemologyPsychoanalysisPhilosophy

Abstract

fetched live from OpenAlex

Abstract Contemporary phenomenological psychopathology has raised questions concerning selfhood and its possible alterations in schizophrenia spectrum disorders. Although the notion of the self is central to several accounts of anomalies, it remains a question how exactly the radically minimal experiential features of selfhood can be altered. Indeed, the risk is to reduce the notion of selfhood so drastically, that it can no longer account for alterations of experience. Here we propose to give thickness to the minimal self. To do this we first discuss Sartre’s phenomenological definition of coenesthesia as the translucent matter of consciousness articulating it with the notion of existential feelings. We then draw on the historical research on coenesthesia to dig deeper in this translucent materiality identified as the element of the body. We show that selfhood, even in its most minimal level, must be conceived of with a specific elemental thickness, corresponding to a phenomenological materiality of embodiment. We argue that a phenomenological reading of coenesthesia as the bodily element of ipseity can shed a new light on the anomalies of self-experience.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.465
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.007
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.055
GPT teacher head0.305
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2023
Admission routes1
Has abstractyes

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