The social drama of digital multimodal composing: A case study with emergent bi/multilingual newcomer students
Bibliographic record
Abstract
Abstract This multi‐year, ethnographic, qualitative case study in English Language Learning classrooms contributes a unique analysis of nine adolescent newcomer students' investment in a digital multimodal composing (DMC) project as a social drama. Using reflexive thematic analysis, it explores the following possibilities afforded by in‐school, dramaturgically structured DMC processes for the students' investment in classroom learning: (1) changing the definition of the situation, (2) supporting students' impression management to gain social and cultural capital, and (3) creating bonds of reciprocal dependence and familiarity. The study helps language and literacy researchers, educators, and teacher educators better understand emergent bi/multilingual newcomer students' investment in DMC processes through the sociological perspective of dramaturgy, suggesting how DMC might deepen learning while valuing the assets of culturally, linguistically, and racially diverse newcomer students.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".