COMMENT ÉLABORER UN PLAN DE COURS DE LANGUES DE SPÉCIALITÉS POUR LES ÉTUDIANTS EN LEA? / HOW TO DESIGN A SPECIALIZED LANGUAGES COURSE PLAN FOR MODERN APPLIED LANGUAGES STUDENTS? / CUM SǍ ELABOREZI UN PLAN DE CURS DESPRE LIMBAJELE DE SPECIALITATE PENTRU STUDENŢII DE LA LIMBI MODERNE APLICATE
Bibliographic record
Abstract
The present article aims to offer an analysis of the importance we give to designing a course plan in the field of specialized languages, for Modern Applied Languages students. It is in fact a document associated with such notions as transparency, interaction, communication and, in order to reach a correct and coherent acquisition of knowledge, it must be drawn up in collaboration with the other course coordinators. It is a document which is to be found under different names in higher education institutions in Europe, Canada or the United States. However, it is compulsory that it contains the following items: required background knowledge, the content of the course, the target level, evaluation methods, bibliography, etc. Given the current context in which the higher education is placed all over the world and the rapid changes in the labour market, the content of the course will often be submitted to changes and comebacks for each course unit.
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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.013 |
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".