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Record W4309005568 · doi:10.1017/s1470542722000022

A Variationist Sociolinguistic Analysis of Intensifiers in Oslo Norwegian

2022· article· en· W4309005568 on OpenAlexaff
James M. Stratton, John D. Sundquist

Bibliographic record

VenueJournal of Germanic Linguistics · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicLinguistic Variation and Morphology
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsNorwegianAdjectiveAdverbPredicative expressionLinguisticsPsychologyVariation (astronomy)SociolinguisticsAttributiveDemonstrativeGermanNounPhysicsPhilosophy

Abstract

fetched live from OpenAlex

The present study uses variationist sociolinguistic methods to examine the intensifier system in Oslo Norwegian. Results indicate that both linguistic and social factors influence intensifier use. Predicative adjectives were intensified more frequently than attributive adjectives, women used intensifiers more frequently than men, and younger speakers had higher intensification rates than older speakers. Apparent time analyses also reveal a change in progress toward the use of skikkelig ‘proper’, a change led predominantly by young women. Although veldig ‘very’ was the most frequently used intensifier, its use decreases in apparent time, whereas skikkelig increases in frequency among younger speakers. The development of the intensifier skikkelig appears to follow a common pathway of change from adjective to manner adjunct to degree adverb, as well as from appropriateness to intensification. Comparisons with work on English, German, and Norwegian reveal several crosslinguistic tendencies about the linguistic and social conditioning of intensifiers. This study provides the first variationist sociolinguistic analysis of intensifiers in Oslo Norwegian; it provides support for several crosslinguistic claims about intensifier use; and it contributes to the visibility of variationist sociolinguistic work in the study of Norwegian variation and change.

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)0.003
metaresearch head score (Gemma)0.024
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.822
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.326
Teacher spread0.300 · 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.

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

Citations12
Published2022
Admission routes1
Has abstractyes

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