Process and Product in ISLA Research: Courage, Commitment, and Tolerance for Ambiguity
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.
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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 | 0.112 | 0.233 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.028 |
| Scholarly communication | 0.019 | 0.010 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".