MétaCan
Menu
Back to cohort
Record W3094746467 · doi:10.1075/ml.00012.tar

On <i>twittizens</i> and <i>city residents</i>

2020· article· en· W3094746467 on OpenAlexaff
Elizaveta Tarasova, Natalia Beliaeva

Bibliographic record

VenueThe Mental Lexicon · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicLinguistic Variation and Morphology
Canadian institutionsAccess Alliance Multicultural Health and Community Services
Fundersnot available
KeywordsSemantic propertyNatural language processingCompoundingSemantic interpretationComputer sciencePerceptionInterpretation (philosophy)ViewpointsTransparency (behavior)LinguisticsArtificial intelligenceInformation retrievalPsychologyMaterials sciencePhilosophy

Abstract

fetched live from OpenAlex

Abstract The present study analyses native speaker perceptions of the differences in the semantic structure of compounds and blends to specify whether the formal differences between compounds and blends are reflected on the semantic level. Viewpoints on blending vary, with some researchers considering it to be an instance of compounding ( Kubozono, 1990 ), while others identify blending as an interim word formation mechanism between compounding and shortening ( López Rúa, 2004 ). The semantic characteristics of English determinative blends and N+N subordinative compounds are compared by evaluating the differences in native speakers’ perceptions of the semantic relationships between constituents of the analysed structures. The results of two web-based experiments demonstrate that readers’ interpretations of both compounds and blends differ in terms of lexical indicators of semantic relations between the elements of these units. The experimental findings indicate that language users’ interpretation of both compounds and blends includes information on semantic relationships. The differences in the effect of the semantic relations on interpretations is likely to be connected to the degree of formal transparency of these units.

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 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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.828
Threshold uncertainty score0.363

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 head0.039
GPT teacher head0.309
Teacher spread0.270 · 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 teacher head, 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

Citations1
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueThe Mental LexiconSame topicLinguistic Variation and MorphologyFrench-language works237,207