Legitimising linguistic devices in A Cheering Voice from Upper Canada (1834)
Bibliographic record
Abstract
The present study categorises those devices concerning the expression of point of view in A cheering voice from Upper Canada (Colborne 1834). This book gives information about the English colony in Canada that is of value for prospective emigrants. The intention of the writer is to provide a detailed account of the benefits of living in Upper Canada, and why this is the ideal place for making profit. In addition, the writer has a very clear picture of the type of emigrants he envisages for the colony. Our objective is to explore legitimising devices in this book. Legitimising devices for us concern all those strategies leading to defence, support or justification of point of view. In this sense, this study covers matrices as well as other linguistic devices that are somehow indexical of the author’s position in the text. Our method follows from the model for the study of stancetaking proposed in Marín-Arrese (2009). We conclude that the author uses deontic modals to show authoritative voice whereas epistemic modality and internal/external participant modality have the persuasive function of promoting migration to Upper Canada. Moreover, the use of epistemic modals along with attitudinal expressions, communicative evidentials and cognitive matrices indicate a contrast between Canadian and old European social practices. The combination of these devices clearly leads to the fulfilment of the author’s intentions explicitly given in the introductory paragraphs of his book.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.017 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".