Reading Humanitarian Heroes for Global Citizenship Education?: Curriculum Critique of a Novel Study on Craig Kielburger's "Free the Children".
Bibliographic record
Abstract
Literature classrooms hold great potential to educate students for critical global citizenship through serious engagement with marginalized stories that test or subvert mainstream knowledges and structures, including the familiar humanitarian framework that dominates Western thinking about the Global South. Unfortunately, much existing literary curriculum in the Global North often does just the opposite. Instead, Western-oriented texts and safe, traditional reading practices contribute to a form of global citizenship that perpetuates Western hegemony and limits expressions of citizenship to benevolent actions. This is especially the case where global citizenship curriculum is developed by NGOs and humanitarian organizations, such as Me to We, a popular social enterprise with increasing influence over education in Canada, the U.S. and the U.K. Using the frameworks of critical global citizenship education, Slaughter’s (2006) theory of humanitarian reading, and Stone-Mediatore’s (2003) notion of reading for enlarged thought, this paper will undertake a close reading of the unit materials for Free the Children, a unit developed by Me to We, which aspires to educate for global citizenship. Unit activities problematically appropriate the voices and viewpoints of child laborers in South Asia by establishing dichotomies between readers and the populations that Me to We aspires to help. This unit provides a means by which to examine the effectiveness of reading a memoir by an exemplary humanitarian, particularly when unit activities are framed by an organization with a particular humanitarian agenda.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.031 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".