Anti-oppressive global citizenship education in English language teaching: a three-pillar approach
Bibliographic record
Abstract
Anti-oppressive global citizenship education (GCE), a specific strand of critical GCE, is a new field, especially concerning empirical studies within English classrooms. Based on an anti-oppressive GCE framework and the research question, “what does anti-oppressive theory look like in practice in English classrooms and how can this be woven into GCE?”, this paper explains the results of a project which used a portraiture methodology to collect and analyze approximately 6 hours of semi-structured interviews, detailed impressionistic records, and several lessons collected with one secondary school English teacher in Ontario, Canada. The portrait showcases how the educator implements a three-pillar approach to anti-oppressive GCE language education and the need to shine light on minoritized identities, create healthy soil for the foundation of learning about systemic oppression, and give the proper amounts of water/support to each student.
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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 | 0.010 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.010 | 0.082 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".