The need for critical digital literacies in generative AI-mediated L2 writing
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
This article asserts that the use of generative AI (GenAI) technologies for L2 writing needs to involve critical digital literacies. Drawing on the initial insights from a case study exploring the GenAI practices of secondary school students in Canada, this paper highlights emergent issues surrounding the dispositions of these learners towards these tools, the designs of platforms, and the material differences in the way these tools generate responses and encourage specific practices. Recognizing the inequalities that circumscribe the use of these technologies, this paper proposes materiality , indexicality , and ideology as key constructs that help develop an understanding of critical digital literacies relevant to GenAI-mediated L2 writing and digital multimodal composing. These constructs draw attention to how platform designs and other material processes, together with learner access to resources, can steer learners toward particular interactions and discourses. By understanding how GenAI platforms trained on large datasets can privilege certain ways of thinking and writing, L2 writers can develop a more critical perspective of how these technologies can shape the way we write ourselves into being.
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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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