Bibliographic record
Abstract
Needs analysis and description of language use in target situations are used to define the communicative demands of the target situation (Basturkmen & Elder, 2004). In spite of these steps taken in designing LSP courses, many L2 learners still face communication challenges in the workplace. Such is the case of L2 nursing students who experience various language related issues in their clinical placements. These results are not surprising given that L2 instruction traditionally targets the acquisition of the standard norm of the target language (Valdman, 2000). Indeed, L2 textbooks appear heavily influenced by the prescriptive view of language. This study focuses on the content analysis of French L2 commercial textbooks for nursing students. The materials were analyzed to identify the language use domains presented to students as well as the language features recommended to perform them. The results were then compared to transcriptions of 15 hours of recorded professional interactions between bilingual French-English nurses and French-speaking patients in a nursing home in Western Canada. The analysis reveals that commercial materials do not fulfill students’ communicative needs. The concept of pedagogical norm (Valdman, 1976, 2000) appears as a useful tool to improve the communicative content of LSP textbooks.
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 | 0.004 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".