‘Un futuro mejor para todos’: Towards a critical humanizing English language teaching
Bibliographic record
Abstract
During more than 50 years of socio-political unrest in Colombia, extreme violence has profoundly affected marginalized students in public schools. Although these topics have been mainly addressed by history and social studies teachers, English language teaching (ELT) has paid little attention to addressing issues of social injustice in the class. To fill this gap, this critical ethnography looks at how a social justice curriculum has been used in ELT classes to empower students to learn skills that allow them to discuss the violence that occurs both inside and outside of the school environment. The fieldwork was carried out in a public high school for eight months, in Bogotá (the capital of the country) with three English teachers and their young students. Data was collected through focus groups, interviews and classroom observations and then analysed using thematic analysis. The findings of the study revealed that the activities suggested by the teachers proposed a change in teaching pedagogies toward solving social problems in students’ communities. The findings further suggest that a negotiated curriculum with the students fosters a critical humanizing pedagogy that promotes social cohesion, and as a consequence improves language learning.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.022 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".