Demands on and of Citizenship and Schooling:“Belonging” and “Diversity” in the Global Imperative
Bibliographic record
Abstract
Educational theory and practice are contending with a sense that it is imperative to take-up “the global” in schools so as to promote a sense of global responsibility and global consciousness. A review of contemporary academic literature reveals particular tensions marking the mutually reinforcing relationships between citizenship, diversity, and schooling. A main theme of this paper is the increasingly complex notion of “belonging” integral to democratic citizenship and the related questions of inclusion and exclusion inherent to citizenship and schooling. By demonstrating that, despite particular paradoxes, citizenship continues to be called on as an ideal through which to push for social justice on local and global levels, the paper contends that a great deal is demanded of citizenship and citizenship education. This paper argues for a new, flexible theory of citizenship that interrogates the assumptions on which a “neutral” notion of citizenship is based. In examining what is demanded of citizenship, the paper looks at what demands must be made of a notion of citizenship. The paper ends with a strong consideration of global citizenship education as an educational response to the global imperative.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.066 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".