Bibliographic record
Abstract
The online activities of new English language learners can reveal rich and varied literary behaviors, which are almost invisible in the middle grade classroom. While these non-native English speakers may experience cultural and linguistic apartness and struggle to express their identities at school, many develop online identities using their literacy skills in a highly productive, engaged, and anonymous fashion. When viewed through a New Literacies (Gee, 2000; Street, 1995) and Multiliteracies (Cope & Kalantzis, 2000) perspective, closer analysis of the predispositions, social attitudes, and activities of these students reveals significant educational advantages that may go largely undetected by educators in the traditional classroom. This article presents a qualitative case study, involving two English language learners, who actively sought out and engaged in online spaces where they could establish identities, practice multimodal literacies, and seek out affinity groups in keeping with their personal interests and abilities. This research is of significance to educators as it demonstrates the manner in which digital technologies can provide equitable access to literate practices for English Language Learners in the classroom.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.019 | 0.014 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".