Thinking‐Aloud as Talking‐in‐Interaction: Reinterpreting How L2 Lexical Inferencing Gets Done
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
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.
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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