Introduction to the Special Issue
Bibliographic record
Abstract
Research on written corrective feedback (WCF) has become central to our understanding of how writing skills develop and how language learners respond to instruction.As writing instruction shifts toward more student-centered, process-oriented approaches, the need to examine how learners interpret, engage with, and apply feedback has grown.WCF research provides insights into these processes and contributes not only to second language acquisition theory but also to improvements in writing pedagogy across diverse contexts.This special issue brings together a selection of peer-reviewed papers originally presented at the 1st International Conference on Written Corrective Feedback in L1 and L2, held at the Universitat de Vic-Universitat Central de Catalunya (UVic-UCC) in 2023 (www.wcf23.org).The conference was organized by the Glossa research group of the Universitat de Vic-Universitat Central de Catalunya, with the collaboration of the Elbec research group of the Universitat Autònoma de Barcelona.It was funded by the projects "Mestres i retroacció correctiva escrita/Teachers and written corrective feedback" (2020ARMIF 0025, Secretariat of Universities and Research, Generalitat de Catalunya) and PIRE2021 (CIFE, UVic-UCC) and a grant for the organization of activities in scientific dissemination at UVic-UCC (Vice-Rectorate for Research and Knowledge Transfer).The conference aimed to foster dialogue between researchers and educators engaged in understanding how feedback practices can shape writing development across educational contexts and linguistic backgrounds.The five contributions in this issue reflect the diversity and depth of that dialogue and offer empirical insights into feedback practices, learner engagement, and the dynamics of classroom interaction across first and additional language settings.This special issue is structured around three thematic strands that emerged prominently during the conference: learner engagement and feedback processing, instructor practices and feedback effectiveness, and collaborative dimensions of writing and revision.The volume opens with a foreword by Rosa Manchón, where she reviews developments in WCF research, focusing on learner engagement and contextual variables and how they contribute substantively and methodologically to the field across L1 and L2 contexts.The first study, The differential effect of individual and collaborative processing of written corrective feedback on French as a foreign language learners' engagement, is authored by Lira-Gonzales, Nassaji, and Chao and investigates how learners of French
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.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.344 | 0.231 |
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".