Bibliographic record
Abstract
Diasporic settings confront scholars with mixes among cultures, dialects, and languages. Sociolinguists must describe the specific forms of that mix, then explain it by considering the social dynamics that produce it. This task presents above all a methodological challenge. Most scholars of culture and society agree that diasporic mixing between dialects and languages will become drastically more frequent in the nascent 21st century, and will come to dominate urban sociolinguistic space in many places. We have to ask: what methods are best suited to study the social meanings and structural changes of linguistic forms under conditions of ’super-diversity’ (Blommaert 2010; Vertovec 2007)? And how can we model community-level mixing while adequately accounting for the vast amount of observed idiolectal variation? In work on the Jamaican diaspora, I have conducted video-observations of Reggae and Rap artists of Jamaican heritage in Toronto. This paper presents an example of a frequency-based analysis of vocalic variation and discourse analyses of selected transcripts. Bridging the divide between quantitative and qualitative methodologies in sociolinguistics I employ Bourdieu’s (1982) notion of the symbolic capital of linguistic forms to explain the observable structural features of the diasporic dialect mix that occurs in the Jamaican Canadian community.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.017 | 0.011 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".