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Record W6910481411 · doi:10.48448/54zy-vt20

All Animals are Conscious: A New Premise in the Cognitive Science of Consciousness

2022· other· en· W6910481411 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueUnderline Science Inc. · 2022
Typeother
Languageen
Field
Topic
Canadian institutionsYork University
Fundersnot available
KeywordsPremiseConsciousnessCognitionAnimal cognitionCognitive systemsKey (lock)

Abstract

fetched live from OpenAlex

The Distribution Question (DQ) asks: Can we know which animals beside humans are conscious? The current best practices for answering DQ rely on a version of a marker approach, which seeks to find some observable features that indicate consciousness. I argue that the marker approach all but guarantees a positive answer regarding any animal selected for investigation-including sponges. This makes answering the DQ currently unachievable. What will turn it into an achievable goal is to have a robust theory of consciousness, which, I argue, requires we adopt the premise that all animals are conscious.

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.

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)0.007
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Open science, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.926
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.010
Science and technology studies0.0010.019
Scholarly communication0.0000.000
Open science0.0060.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.038
GPT teacher head0.328
Teacher spread0.290 · 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

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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