Practitioners respond to Suzanne Graham's ‘Research into practice: Listening strategies in an instructed classroom setting’. (2017)
Bibliographic record
Abstract
In her 2017 article ‘Research into practice: Listening strategies in an instructed classroom setting,’ Suzanne Graham outlines ways that research-derived principles of listening instruction have (not) been adopted in second language (L2) classrooms. She organizes her argument into three categories, discussing research findings that have not been well applied, those that have been over-applied, and areas she views as holding good potential for application. In this short response, I compare Graham's conclusions about the extent of research adoption to my own experiences as a language teacher and make additional comments about the application of those research findings in the context of post-secondary L2 English instruction.
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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 | 0.026 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.022 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.006 | 0.017 |
| Research integrity | 0.046 | 0.072 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".