Understanding the linkage gap between L2 education researchers and teachers
Bibliographic record
Abstract
This paper reports the results of a mixed-method study that analyzed second language (L2) teachers’ and researchers’ interactions on an online forum organized to facilitate the discussion of six published articles written by the participating researchers. The project used Lavis et al.’s (2003) knowledge transfer framework and Graham et al. (2006) knowledge to action framework as foundations to create shared space with the view to addressing the linkage gap between L2 researchers and L2 teachers. It specifically created a virtual space for dialogue and brought the two groups together to discuss topics of common interest. The asynchronous interaction produced a text-based set of data that reflected the ideational and linguistic choices of the participants. We use textlinguistic analysis procedures and refer to Gee’s (1990) Discourse theory to interpret these choices and understand how they shaped the direction and content of the interaction between researchers and practitioners.
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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 | 0.161 | 0.237 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.022 | 0.021 |
| Scholarly communication | 0.027 | 0.042 |
| Open science | 0.005 | 0.042 |
| Research integrity | 0.006 | 0.006 |
| 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".