How I learned to read German und so weiter: triangulating perspectives of a grammar translation approach to foreign language reading
Bibliographic record
Abstract
This thesis seeks to explore and understand the foreign language pedagogy, grammar-translation, as an educational experience . Conducted at a Canadian university, the study involved 33 undergraduate and graduate students registered in a 12-week Reading German course. Throughout this period, the researcher, himself a member of the class, kept a language-learning diary in which he recorded his thoughts in relation to what and how he was learning. In addition, at the end of the course, he conducted semi-structured interviews with nine of his peers to better understand how they had engaged in and coped with the German language-learning process. The data collected from both the diary and interviews was analyzed using the qualitative research software, N5®. Such an analysis permitted the identification of common themes across diary entries and interview transcripts which, in turn, served as the basis for developing a theoretical model reflective of the classroom language-learning situation in question.
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 | 0.023 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.018 | 0.031 |
| Scholarly communication | 0.021 | 0.016 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".