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Record W2309753735 · doi:10.1017/s0142716416000096

Quantifying semantic animacy: How much are words alive?

2016· article· en· W2309753735 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueApplied Psycholinguistics · 2016
Typearticle
Languageen
FieldPsychology
TopicCategorization, perception, and language
Canadian institutionsUniversity of Alberta
FundersScience and Engineering Research BoardNatural Sciences and Engineering Research Council of Canada
KeywordsAnimacySerbianCategorizationPsychologyNormativeLinguisticsPremiseCognitive psychologyMeaning (existential)

Abstract

fetched live from OpenAlex

ABSTRACT The main goal of this study, which comprised two experimental tasks and three normative studies, was to describe the underlying distribution of semantic animacy, with the focus on Serbian and English. Animacy was measured using three normative techniques. The cognitive effects of obtained measures were tested in two experiments conducted in both Serbian and English: a visual lexical decision task and a semantic categorization task. Results suggest that semantic animacy is a graded property. A high correlation between Serbian and English measures suggests that semantic animacy might be language independent, most likely because of its biological grounding. As for its behavioral correlates, animacy does not affect lexical decision times but it does codetermine the categorization speed: the category decision gradually slows as a function of the degree of animacy. These results were consistent across two languages under research scrutiny. We thus conclude that animacy is a continuous aspect of meaning.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.768
Threshold uncertainty score1.000

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)0.0040.002

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 head0.052
GPT teacher head0.338
Teacher spread0.287 · 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