Bibliographic record
Abstract
Five articles (one in French and four in English) and two book reviews comprise this first 2023 regular issue (26, 1) of the Canadian Journal of Applied Linguistics.The papers address a wide variety of topics that range from the impact of language proficiency testing on newcomers' settlement efforts, the efficacy of reading comprehension tests, the effects of language policy on one's access to language education, to the role of video feedback and lexical knowledge on language teaching and learning.McLeod considered the utility and fairness of the English language proficiency testing practices in determining immigration, asylum and resettlement, and citizenship outcomes in Canada, the United Kingdom, Australia, and the United States.After a careful examination of the ways in which each country assesses migrants' proficiency, the paper highlights the factors the governments have used to determine migration targets and influence the effectiveness of test results.The paper concludes with a discussion of the Canada-based challenges and offers directions for future research and advocacy initiatives.Maintaining the focus on Canada and testing, Turcotte, Prévost, and Caron report on the development and evaluation of two French-medium reading comprehension tests (with each centering either on a narrative or informational text) to determine their effectiveness for teaching and research purposes.After 401 French-speaking children took the tests, the results were examined using factorial and multilevel analyses.The findings demonstrated that while both tests were similar in the factorial structure, some items on the informational text test may require modification.Using a combined lens of critical discourse analysis, interpretive analysis, and critical sociocultural analysis, Kunnas examined various publicly available provincial (Ontario) and regional policies, curricula, and other related documents to determine the kind of student that French Immersion programs attract and ultimately serve.The analysis shows a clear preference for an "elite" student, characterized as being White, Canadian born, middle class, and English speaking, and has prompted the author to call for more democratic policies, systems, and funding that are inclusive of English language learners and students with special educational needs.To determine the effectiveness of video-based technology as a feedback tool, Park recruited eight tertiary-level language teachers from the UK and South Korea to first supply corrective information on their learners' pre-recorded oral production and to then reflect on the experience.Using the Video Enhanced Observation (VEO) application, the teachers watched the students' videos and commented on all (linguistic and non-linguistic) areas they deemed necessary.The comments were tagged on a timeline that both the teachers and learners could access.The teachers proved highly receptive to this type of technology, praising its numerous affordances, including the user-friendly interface, and the positive impact it brought to their feedback practice.Ait Hammou, Larouz, Fagroud, and Akki investigated the extent to which lexical knowledge at the level of lexical sophistication (operationalized in terms of lexical diversity, CJAL *
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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 | 0.007 | 0.040 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.015 | 0.012 |
| Insufficient payload (model declined to judge) | 0.088 | 0.060 |
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".