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Record W2065143701 · doi:10.1075/ml.4.1.03gag

Does s <i>now man</i> prime <i>plastic snow</i>?

2009· article· en· W2065143701 on OpenAlexaff
Christina L. Gagné, Thomas L. Spalding, Lauren Figueredo, Allison C. Mullaly

Bibliographic record

VenueThe Mental Lexicon · 2009
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsRelation (database)Prime (order theory)Priming (agriculture)NounInformation processingComputer scienceArtificial intelligenceNatural language processingLinguisticsMathematicsPsychologyCognitive psychologyData miningCombinatoricsPhilosophyBiology

Abstract

fetched live from OpenAlex

Three experiments were conducted to determine the extent to which relational and morphosyntactic information influence the processing of modifier-noun phrases. Processing of the target was faster when the shared constituent was in the same position in both the prime and the target, regardless of whether the relation was the same or different. In contrast, relation priming was contingent on the morphosyntactic role of the shared constituent; repeating the relation with the constituent in a different morphosyntactic role did not speed processing of the target (Experiments 1–3) whereas repeating the relation with the constituent in the same role did speed processing (Experiments 3). These results suggest that conceptual information is accessed in light of the constituent’s particular morphosyntactic role.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.002

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 head0.016
GPT teacher head0.266
Teacher spread0.250 · 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 designObservational
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

Citations25
Published2009
Admission routes1
Has abstractyes

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