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Record W2017611320 · doi:10.1017/s0022226714000061

A ‘Galilean’ science of language

2014· article· en· W2017611320 on OpenAlexaff
Christina Behme

Bibliographic record

VenueJournal of Linguistics · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicLanguage and cultural evolution
Canadian institutionsDalhousie University
Fundersnot available
KeywordsTransformational grammarScholarshipCriticismAppealLinguisticsTheoretical linguisticsEpistemologyAnthropological linguisticsRelation (database)Applied linguisticsSociologyPsychologyGrammarPhilosophyClinical linguisticsComputer scienceLaw

Abstract

fetched live from OpenAlex

The Science of Language , published in the sixth decade of Noam Chomsky's linguistic career, defends views that are visibly out of touch with recent research in formal linguistics, developmental child psychology, computational modeling of language acquisition, and language evolution. I argue that the poor quality of this volume is representative of the serious shortcomings of Chomsky's recent scholarship, especially of his criticism of and contribution to debates about language evolution. Chomsky creates the impression that he is quoting titbits of a massive body of scientific work he has conducted or is intimately familiar with. Yet his speculations reveal a lack of even basic understanding of biology, and an unwillingness to engage seriously with the relevant literature. At the same time, he ridicules the work of virtually all other theorists, without spelling out the views he disagrees with. A critical analysis of the ‘Galilean method’ demonstrates that Chomsky uses appeal to authority to insulate his own proposals against falsification by empirical counter-evidence. This form of discourse bears no serious relation to the way science proceeds.

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)0.005
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0050.034
Scholarly communication0.0090.011
Open science0.0020.004
Research integrity0.0040.009
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 head0.013
GPT teacher head0.333
Teacher spread0.320 · 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 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

Citations92
Published2014
Admission routes1
Has abstractyes

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