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Record W2462319635 · doi:10.7202/1099297ar

Qui utilise 10 % de son cerveau ?

2023· article· fr· W2462319635 on OpenAlexaffvenue
Serge Larivée, Jacinthe Baribeau, Jean-François Pflieger

Bibliographic record

VenueRevue de psychoéducation · 2023
Typearticle
Languagefr
FieldNeuroscience
TopicNeurology and Historical Studies
Canadian institutionsUniversité LavalUniversité de Montréal
Fundersnot available
KeywordsPhilosophyHumanities

Abstract

fetched live from OpenAlex

Ce texte examine la croyance largement répandue selon laquelle les humains n’utiliseraient que 10 % de leur cerveau. Le texte comprend quatre parties. Les deux premières parties évaluent l’ampleur du « mythe du 10 % » et en retracent les origines. La troisième partie montre que cette croyance n’est nullement justifiée en passant successivement en revue les connaissances concernant l’anatomie et la physiologie du cerveau, la plasticité cérébrale ainsi que des données sur l’évolution. La dernière partie évoque quelques raisons de la persistance du mythe du 10 %.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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: Empirical
Teacher disagreement score0.325
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.005

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 head0.079
GPT teacher head0.345
Teacher spread0.266 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations1
Published2023
Admission routes2
Has abstractyes

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