Acquiring and maintaining second-language skills: An examination of Canadian federal public service programs
Bibliographic record
Abstract
Research has shown that although it takes time and effort to acquire additional languages, they are valuable assets. Both teachers and learners have to be motivated, and active participation is required to succeed. Unfortunately, when active training is completed, the acquired skills seem to be easily lost. \nIn this project, I describe specific programs used for the purpose of language training and the goals that are set for the military and civilian second language (L2) learners within the Ministry of National Defence bilingual Canada. I also review relevant literature in order to identify ways to maintain the acquired L2 skills after active learning has ended. \nDuring my literature research, I examined areas that pertain to language acquisition from both teachers’ and learners’ points of view. Teaching methods, testing within the government program, motivation, aptitude, and computer-assisted learning technologies were explored with respect to their use and educational value. Most of the studies that I found in my research indicate that teachers’ and learners’ motivation is an essential factor for success, that L2 is still a developing field where research is insufficient, and that many questions remain concerning retention of acquired L2 skills. \nEven if little research has been conducted on the question of language retention and maintenance to find out the rate at which an L2 is lost, the impression is that to maintain the acquired (L2) skills, teachers’ energies must be focused on ways to promote ongoing maintenance habits right from the beginning.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.007 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".