Bibliographic record
Abstract
The aim of the study was to explore the nature of attitudes towards Jamaican Creole Heritage language instruction in the city of Toronto, as well as how such attitudes were affected by an individual’s level of ethnic orientation, age, and immigrant generation. The study involved 43 individuals between the ages of 18-60+ of Jamaican heritage or who were connected to Jamaican heritage by means relationships. A mixed methods approach to data collection and analysis was employed. Quantitative data was collected through a questionnaire and analysed using multiple regression analysis, while qualitative data was collected through semi-structured interviews and analysed thematically. Overall, findings revealed the complex and multi-layered nature of attitudes towards Jamaican Creole. Though participants largely expressed positive attitudes and feelings towards the language, attitudes towards formal Jamaican Creole heritage language instruction were largely dismissive and did not appear to be mediated by ethnic orientation, age, or immigrant generation. Such findings highlight a need for Jamaican Creole language awareness raising initiatives in Toronto as well as the development of Jamaican Creole literature of more varied genres, particularly those of an academic nature. Such advances may increase the perceived instrumentality of the language and address concerns about standardization, both factors which appear to have some bearing on attitudes to Jamaican Creole heritage language instruction in the city.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".