Quotatives in the Jamaican acrolect : corpus-based variationist studies of vernacular globalisation in World Englishes
Bibliographic record
Abstract
This dissertation is a study of quotatives – innovative, traditional and local – in Jamaican English and draws comparisons with the use of quotatives in Irish English and Canadian English on the basis of parallel corpora of the International Corpus of English (ICE). Special attention is paid to the use of the new quotative be like, which emerged in US English only little more than thirty years ago but has spread extremely rapidly into other varieties since then. Although this is noted in the literature, little is known about its use in postcolonial varieties in which the majority of the population does not speak English as a mother tongue. The dissertation aims to close this research gap by studying the Jamaican quotative system. Combining methods used in corpus linguistics and variationist sociolinguistics, the study shows which linguistic and social factors constrain the use of the most frequent quotatives in the private dialogues of ICE-Jamaica, ICE-Ireland and ICE-Canada. In addition, it offers a qualitative analysis of selected longer extracts from the Jamaican data and examines the social meaning of be like, go, say and seh in Jamaica on the basis of a survey. Quotative use is discussed in the light of grammaticalisation and the globalisation of vernacular linguistic resources. In particular, the study addresses the question whether second-language varieties of English are as open to the spread of the new quotatives as natively spoken ones.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".