A Variationist Sociolinguistic Analysis of Intensifiers in Oslo Norwegian
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".