A sociolinguistic study of English negation in Manitoba
Bibliographic record
Abstract
Negation is a linguistically universal phenomenon (Dahl, 1979); however, it may be expressed differently within and across languages (Miestamo, 2005). This study pursues an explanation of variation in English negation in Manitoba and uses a corpus of interviews recorded in Winnipeg, Steinbach, and Altona-Winkler-Morden. It investigates the variable use of three forms of English negation: no-negation (e.g., I have no food), not-negation (e.g., I don’t have any food) and negative concord (e.g., I don’t have no food). This research concentrates on both linguistic and social factors through the lens of variationist sociolinguistics. It aims to explore how different linguistic factors i.e., verb type and indefinite pronoun and social factors i.e., generation, gender, socioeconomic status, rurality, religious affiliation and first language impact the variation of English negation in Manitoba. This research, in particular, investigates whether there is a change in progress in English negation in Manitoba. The most obvious finding to emerge from this study is that linguistic factors have a more robust effect on the variation of English negation than social factors. While lexical verbs strongly favour not-negation, functional verbs significantly disfavour this variant. This study supports Tottie’s (1991 b) hypothesis that high frequency verbs like functional verbs tend to appear with no-negation and low frequency verbs like lexical verbs favour not-negation. The findings show that although there is no obvious change in progress among generations, there is a split between older generations and younger generations. Low German L1 speakers also prefer no-negation in their conversations more than English L1 speakers. This study suggests that according to shortest path principle (Wald, 1996) these speakers transfer their L1 form of negation into their L2. Location also shows significant impact on the variation of English negation, with Steinbach having the highest rate of no-negation among all locations.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".