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Record W4313418551 · doi:10.1080/09515089.2022.2160700

Where minds begin: a commentary on Joseph LeDoux’s the deep history of ourselves

2022· article· en· W4313418551 on OpenAlexaff
Arthur S. Reber, František Baluška

Bibliographic record

VenuePhilosophical Psychology · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and Biological Electrophysiology Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsConsciousnessSentiencePerceptionSubjectivityCognitive sciencePsychologyFeelingConstructivePropositionEpistemologyQualiaSociologyPhilosophyComputer science

Abstract

fetched live from OpenAlex

We are sympathic with LeDoux’s primary goal here ─ to get a solid scientific grip on what has been dubbed one of the most elusive, important questions in scientific discourse, to identify the underlying biomolecular processes that give rise to consciousness. However, we have issues with the way he goes about it and have tried to present them in a constructive manner. Our commentary is built around our theory of the origins of minds, dubbed the Cellular Basis of Consciousness (CBC), and the empirical research that supports it. The CBC is based on the proposition that life and sentience are co-terminous, that life without subjectivity, feeling, without valenced perception, without the capacity to learn and lay down memories would have been an evolutionary dead-end. It could not have survived in the hostile, chaotic world in flux that dominated our planet four billion years ago. The biological sciences operate on the principle that all species, extant and extinct, evolved from the first prokaryotes. The CBC theory is founded on the principle that all expressions of emotion, perception, and cognition did as well.

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 imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.034
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.034
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0130.024
Scholarly communication0.0100.013
Open science0.0050.004
Research integrity0.0340.059
Insufficient payload (model declined to judge)0.0040.002

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 head0.049
GPT teacher head0.263
Teacher spread0.214 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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

Citations2
Published2022
Admission routes1
Has abstractyes

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