Process and Product in ISLA Research: Courage, Commitment, and Tolerance for Ambiguity
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
Abstract Stern (1983) reminds us of the ethical reasons for doing second language (L2) research. That is, given the considerable human and financial investments that go into language education, the practical activities of teaching “should not exclusively rely on tradition, opinion, or trial‐and‐error but should be able to draw on rational enquiry, systematic investigation, and, if possible, controlled experiment” (p. 57). Elsewhere Stern argues for the use of interdisciplinary teams to carry out such research. The studies in this special issue illustrate the aptness of Stern's advice. These articles present findings from a large‐scale classroom research project that compared a deductive approach to teaching Spanish grammar to guided induction using the PACE model. The multidisciplinary team made use of different types of data, which were examined through different theoretical lenses. This discussion article considers the implications of these studies for L2 research, educational practice, teacher education, and the relationship between theory and practice.
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 | 0.002 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it