Bibliographic record
Abstract
The primary purpose of this article is to examine the Canadian image in political discourse, to identify the factors creating this image. Due to the most relevant political speeches, messages, government reports, analysis of statistical data there are being worked out some tools for building the image of Canada on the world stage. Lately, political discourse is becoming a subject of study for linguistics. The interest in the phenomenon is motivated by a wide range of linguistic instruments used as an effective tool to manage the public perceptions. Study and analysis of the mechanisms of political communication allow to decode pragmatic information hidden in the political texts. The subject of the work consists in highlighting the linguistic approaches to the representation of Canada in the international diplomacy. The object of the work is an examination of political discourse in the public statements made by political figures, government documents, which reflect social and political realities. The methodology has developed under the influence of cognitive linguistics of domestic and foreign scientists. Research study includes cognitive and conceptual analysis to reveal distinctive features of the social and political realities; interpretive and context analysis to select political texts. It exposes the ways of representation of Canadian image, demonstrates its weak and strong sides. A vast factual material reveals multi-faced nature of Canada and provides an identity set of lexical units which are reflected in political discourse. The material illustrates today’s image of the country and the perceptions of Canada throughout the world. The idea of multiculturalism is justified by the guiding ideology of ethno-cultural plurality. The main sources of information are official government documents (laws, acts, reports), statistical data, parliamentary debates, party agenda, political speeches. All the provided material combined mass-media information, the Internet.
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 | 0.003 | 0.006 |
| Science and technology studies | 0.016 | 0.015 |
| Scholarly communication | 0.012 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".