Citizenship education in a transnationalizing world: A comparative perspective
Bibliographic record
Abstract
What does it look like to educate for citizenship in a transnationalizing world? Since the beginning of the nation-state, a goal of public education has been to prepare its populace for citizenship. Over the past decade, flows of migrants, economic crises and concerns about climate change are prompting scholars to consider how to educate for citizenship within a globalizing and interdependent world (Kennedy, 2012). Many scholars argue that traditional models of citizenship are insufficient for the transnational nature of peoples lives, attempting instead to theorize citizenship education within a globalized world (Marshall, 2001; Mohanty, 2004; Rizvi, 2011; Yuval-Davis, 1997). Drawing on the work of critical, post-colonial and feminist theories, this paper will present the early findings from a qualitative study examining how transnationalism is conceptualized in citizenship education policies in Canada, Australia and India. We will conduct a critical discourse analysis using the analytical metaphor of a policy web (Author, 2007) to examine the discursive representations of citizenship in a transnationaling world. In an era where neoliberal and neoconservative discourses of citizenship are being re-asserted in Europe and around the globe, it is crucial to examine how education is constructing notions of belonging in a transnational world.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.017 | 0.028 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".