PECULIARITIES OF PROFESSIONAL TRAINING IN APPLIED LINGUISTICS AT CANADIAN UNIVERSITIES (CARLETON UNIVERSITY)
Bibliographic record
Abstract
The article describes the history of Applied Linguistics development. It is noted that the field of Applied Linguistics not only focuses on native, foreign and second language acquisition, but also examines how language interacts with other areas such as the media and legislation. The programs for training specialists in Applied Linguistics in the leading institutions of higher education in Canada are analyzed. In particular, attention is focused on Carleton University. Bachelor's and Master's programs in Applied Linguistics are studied. A special feature of Carleton University Bachelor’s degree program is that students have the opportunity to choose between a 4-year or 3-year program of study to obtain a degree. In addition, at Carleton University, you can combine a specialization in Applied Linguistics and Discourse Studies with an additional one in modern language studies: American Sign Language, German, Italian, Japanese, Chinese, and Spanish. A feature of Applied Linguistics programs for third- and fourth-year students is the option to choose an accelerated study program at Carleton University. Master’s degree program in Applied Linguistics, conditions and criteria for admission are described and analyzed. It is found out that there are three possible ways to complete the Master's program in Applied Linguistics and Discourse Studies: writing a thesis, a research essay or a coursework paper. It is noted that students are first registered for coursework, but at the end of the first year of the master's program they must submit a proposal for writing a research essay or thesis. It is determined that after completing a master’s level of education, a student has the opportunity to continue their studies by enrolling in Carleton University PhD program, which is unique in North America due to its innovative combination of Applied Linguistics and Discourse Studies.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.016 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".