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Record W1485933468 · doi:10.5964/bioling.8759

The Biological Nature of Human Language

2010· article· en· W1485933468 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

VenueBiolinguistics · 2010
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsUniversité du Québec à Montréal
FundersUniversitat de BarcelonaUniversity of OxfordUniversity of Connecticut
KeywordsPsycholinguisticsCognitive scienceHuman languageCognitionComputer scienceLanguage technologyLinguisticsLanguage and Communication TechnologiesPsychologyComprehension approachNatural languageArtificial intelligenceNeuroscience

Abstract

fetched live from OpenAlex

Biolinguistics aims to shed light on the specifically biological nature of human language, focusing on five foundational questions: (1) What are the properties of the language phenotype? (2) How does language ability grow and mature in individuals? (3) How is language put to use? (4) How is language implemented in the brain? (5) What evolutionary processes led to the emergence of language? These foundational questions are used here to frame a discussion of important issues in the study of language, exploring whether our linguistic capacity is the result of direct selective pressure or due to developmental or biophysical constraints, and assessing whether the neural/computational components entering into language are unique to human language or shared with other cognitive systems, leading to a discussion of advances in theoretical linguistics, psycholinguistics, comparative animal behavior and psychology, genetics/genomics, disciplines that can now place these longstanding questions in a new light, while raising challenges for future research.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.845
Threshold uncertainty score0.520

Codex and Gemma teacher scores by category

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

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 head0.014
GPT teacher head0.345
Teacher spread0.331 · 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