Thinking‐Aloud as Talking‐in‐Interaction: Reinterpreting How L2 Lexical Inferencing Gets Done
Bibliographic record
Abstract
There is a general consensus among second‐language (L2) researchers today that lexical inferencing (LIF) is among the most common techniques that L2 learners use to generate meaning for unknown words they encounter in context. Indeed, claims about the salience and pervasiveness of LIF for L2 learners rely heavily upon data obtained via concurrent think‐aloud (TA) research methods. However, despite the consensus that L2 LIF involves a combination of cues, knowledge, and contextual awareness, a crucial aspect of that “context”— namely, the in situ context of TA data collection procedures themselves—is rarely, if ever, included in analyses presented in L2 LIF research studies. I argue in this article that acknowledging this reality and incorporating aspects of this in situ context into analysis is both important and desirable, as it would contribute vital elements of research transparency and legitimacy as well as a much needed reflexivity about claims regarding L2 LIF that are made based on TA data.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".