Ethics, Relationality and Global Citizenship Education: Decolonial Possibilities within Complicity
Bibliographic record
Abstract
In this paper, I engage in a critical and decolonial approach to theorize possibilities in global citizenship education in the Canadian K-12 educational context and teacher education. Recognizing the diverse imaginings in global citizenship education, I explore theorizations within decolonial and critical scholarship that can bring a specific decolonial imaginary to the field in the Canadian context. Through this lens, I share my understanding of global citizenship education as a pedagogical engagement where students would be encouraged to recognize their ‘ethical relationality’ (Donald, 2012) in the world to people, communities, land and more-than-human others. To draw out these ideas in the context of practice, I re-turn (Barad 2014) to a particular story of my own complicity as a white Settler educator within the complex dynamics of embodied experience of societal oppression and privilege within the presumed emancipatory space of education. Through this re-turning, I consider a path in global citizenship education that deconstructs dominant narratives of Canadian exceptionalism and colonial relations, and recognizes the very different experiences in Canada of coming to terms with what it means to be ethically related as a global citizen.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.131 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".