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Record W2053922243 · doi:10.1075/ml.9.1.03spa

Relational diversity affects ease of processing even for opaque English compounds

2014· article· en· W2053922243 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

VenueThe Mental Lexicon · 2014
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMeaning (existential)Process (computing)ComprehensionComputer scienceCompetition (biology)Diversity (politics)LinguisticsRelation (database)PsychologyNatural language processingSociologyBiologyPhilosophyData miningProgramming language

Abstract

fetched live from OpenAlex

Recent research has indicated that understanding compound words involves an attempt at semantic composition of the constituent words, and that this meaning construction process involves an attempt to identify a relation linking the constituents. Research with novel compounds, where a meaning construction process is necessary, has shown that relational interpretations compete to be selected during comprehension, and that increased competition leads to increased processing difficulty. The current project investigates relational competition during the processing of transparent and opaque English compounds. The results show that the diversity of possible relational interpretations affects the ease with which participants can make a lexical decision for a compound. This is true even for opaque compounds, where the identification of the meaning of the compound cannot, by definition, be the result of the meaning construction process. This suggests that initiation of the meaning construction process is obligatory during compound processing.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.259

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 communication0.0000.000
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 head0.046
GPT teacher head0.274
Teacher spread0.228 · 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