[JLIE_898] The Situated Multiliteracies Approach to Classroom Participation
Bibliographic record
Abstract
FULL TITLE: The Situated Multiliteracies Approach to Classroom Participation: English Language Learners’ Participation in Classroom Mathematics Practices Abstract: Guided by sociocultural theory and the theory of multiliteracies, learning is perceived as a shifting participation in practices, which is mediated by multiple physical and symbolic tools. Drawing on the situated multiliteracies approach, which integrates these two theories, the purpose of this ethnographic research is to examine English language learners’ (ELLs) participation in mathematics practices in an urban Canadian classroom. While the classroom examined in this study was limited in explicit language instruction, the classroom environment was loaded with multiple physical and symbolic tools. This study utilizes the situated multiliteracies approach to highlight the contexts in which ELLs were successful in participating in classroom mathematics practices and also how they were able to access an identity as an important participant in the community of practice. Keywords: sociocultural theory, multiliteracies, English language learners, classroom mathematics learning, multilingual classroom Word Count: 5,765 words
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.030 | 0.003 |
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".