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Record W575569921 · doi:10.1017/cbo9780511498473

Information and Meaning in Evolutionary Processes

2004· book· en· W575569921 on OpenAlexaff
William F. Harms

Bibliographic record

VenueCambridge University Press eBooks · 2004
Typebook
Languageen
FieldSocial Sciences
TopicEvolutionary Game Theory and Cooperation
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsReplicator equationMeaning (existential)Information flowInformation transferSelection (genetic algorithm)PopulationInformation theoryEvolutionary dynamicsEvolutionary theoryEvolutionary game theoryIndex (typography)Computer scienceEpistemologyTheoretical computer scienceSociologyMathematicsMathematical economicsArtificial intelligenceGame theoryLinguisticsPhilosophyStatisticsWorld Wide Web

Abstract

fetched live from OpenAlex

This book is intended to help transform epistemology - the traditional study of knowledge - into a rigorous discipline by removing conceptual roadblocks and developing formal tools required for a fully naturalized epistemology. The evolutionary approach which Harms favours begins with the common observation that if our senses and reasoning were not reliable, then natural selection would have eliminated them long ago. The challenge for some time has been how to transform these informal musings about evolutionary epistemology into a rigorous theoretical discipline capable of complementing current scientific studies of the evolution of cognition with a philosophically defensible account of meaning and justification.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.992
Threshold uncertainty score0.738

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.012
GPT teacher head0.208
Teacher spread0.196 · 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.

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

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

Citations61
Published2004
Admission routes1
Has abstractyes

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