SO VERY REALLY VARIABLE: SOCIAL PATTERNING OF INTENSIFIER USE BY NEWFOUNDLANDERS ONLINE
Bibliographic record
Abstract
Previous studies have shown that one of the linguistic tools that individuals use to identify themselves is adjectival intensification (e.g., so cool, very cool, really cool).We assembled and analyzed a corpus of over 3000 intensifiable adjectives (i.e., environments where intensification could occur) extracted from Newfoundland-oriented public internet forums.Statistical analysis of the correlations between intensifier choice social patterning among our speakers led to findings similar to previous studies.So is the most common variant, especially among urban females, while the older variants really and very are favoured in rural areas, especially among males.Conditioning of the less frequent variant fuckin( g) seems to show the persistence of gender distinctions outside urban areas.Mots-des: Variation en anglais, intensificateurs, pratiques langagieres sur internet, Terre-Neuve RESUME Les investigations faites a present confirment que l'intensification adjectivale (e.g., en angl., so cool, very cool, really cool) sert a renforcer l'identification linguistique de I'interloctueur.La presente etude traite une base de plus de 3000 donnees que nous avons rassemblees a partir des salles de discussion sur internet en Terreneuve, adjectifs sujets a I'intensification dans Ie discours populaire.L'analyse statistique des correlations entre les comportements linguistiques dans Ie choix de l'adjectif intensifiant chez nos sujets donne des resu1tats similaires a ceux des etudes anteurieures : So est la variante la plus commune, en particulier chez les femmes urbaines, tandis que les variantes plus vieilles really et very sont favorisees dans les secteurs ruraux, en particulier chez les hommes.Le conditionnement de la variante fuckin( g) parai't indiquer la persistence du role du genre dans la variation Iinguistique en dehors des regions urbaines.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".