11th Graders Acknowledgment of Their Community Through Multiliteracies in an EFL Classroom
Bibliographic record
Abstract
Giving worth to students’ local realities as a background to get meaningful learning not only in the English class but in all the subjects and go further the simple lesson that starts and finishes inside the school walls, is what school communities should expect from education. As Hawkins states: “Learning is enhanced when teachers invite and acknowledge the knowledge, beliefs, and experiences that students bring with them into the classroom” (Bransford, Brown, & Cocking, 2004). This study reports a pedagogical involvement into students’ closest contexts, the school, and their neighborhood, to depict eleventh-grade students' perception of their community context through inquiry in a public school, which is evidenced by the use of multiliteracies. Throughout this study, English was used as a means to communicate what the students found while mapping and observing both contexts, by making connections between the subject syllabus and the findings they made as a result of their local explorations. Data collected from students’ artifacts, the teacher's journal, and surveys showed the student's growing interest in their contexts' recognition which was paramount to make them feel like part of the change in their community contexts. Careful reflections upon findings during the students’ community mapping at their school and neighborhood, encouraged their participation in classroom projects, boosting their critical consciousness by recognizing and assuming a new transformative role that positioned them with a different perspective.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".