Reflecting on the Place of Stories in Global Citizenship Education
Bibliographic record
Abstract
This study proceeds from the presupposition that stories could play a significant role in making children global citizens. Global citizens should possess global awareness, value empathy, acknowledge and value diversity, social justice, environmental sustainability, intergroup helping, and work towards making the world a better place. Following Nussbaum’s (2008) concept of narrative imagination, I argue that telling children stories is one of the most appropriate ways to develop in children the values that would help them to become global citizens. I argue that stories equip children with the appropriate dispositions to make meaningful contributions to the societies in which they find themselves. I also argue that stories provide teachers with a viable method of making their classrooms culturally responsive to the needs of their students. I conclude by indicating that the ambiguity regarding which values to emphasize can be cleared if we give more prominence to the liberal arts, particularly literature, and make more use of stories in the classroom. While stories remain an important ingredient in the class, other ways of knowing such as fieldtrips to museums, castles, and other important historical sites can be used together with stories to help children learn values.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.038 |
| Scholarly communication | 0.012 | 0.020 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".