Efficiency in Second Language Vocabulary Learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
An ongoing question in second language vocabulary learning is how to optimize the acquisition of words. One approach is the so‐called “spaced repetition technique” that uses intervals to repeat words in a given time frame (Balota et al., ; Leitner, ; Oxford, ; Pimsleur, ; Roediger & Karpicke, ; Schuetze & Weimer‐Stuckmann, 2011). Part of the discussion is on the number of words that can be acquired. Interestingly, within this context a question that has not been explored yet is: Is it more beneficial to increase the number of repetitions (while keeping the number of words constant) or to reduce the number of words (while keeping the number of repetitions constant) in order to improve recall rates? This was the premise of the study carried out with beginning learners of German. Results show that reducing the number of words was not as effective as increasing the number of repetitions, a result that is supported by our understanding of how words are processed in the brain, in particular by the phonological loop (Baddeley, ).
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 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 it