Bibliographic record
Abstract
Abstract Out of the four language skills, listening is generally deemed to be the least understood and most under-researched ( Aryadoust, Kumaran et al., 2020 ), partly due to its complex and ephemeral nature. Given that neither the process nor the product of instructed L2 listening can be directly observed ( Brown & Abeywickrama, 2019 ), understanding L2 listening development and learners’ performance on listening tasks poses a number of challenges for language educators and researchers alike. Traditionally, listening has been measured indirectly by analyzing its products, such as responses to comprehension questions, whereas research on the processes underlying L2 listening has been lacking ( Vandergrift, 2010 ). Aiming to encourage the use of process-oriented approaches to investigating L2 listening, this chapter summarizes key research trends that are of particular relevance for ISLA and discusses four main methods that can be utilized for data collection and analysis in process-oriented L2 listening studies. The chapter concludes with step-by-step guidelines for implementing eye tracking into L2 listening studies and some additional recommendations for researchers interested in embarking on this type of research.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.176 | 0.083 |
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".