GLOBALISING CITIZENSHIP EDUCATION? A CRITIQUE OF ‘GLOBAL EDUCATION’ AND ‘CITIZENSHIP EDUCATION’
Bibliographic record
Abstract
: This article discusses, principally from an English perspective, globalisation, global citizenship and two forms of education relevant to those developments (global education and citizenship education). We describe what citizenship has meant inside one nation state and ask what citizenship means, and could mean, in a globalising world. By comparing the natures of citizenship education and global education, as experienced principally in England during, approxim-ately, the last three decades, we seek to develop a clearer understanding of what has been done and what might be done in the future in order to develop education for global citizenship. We suggest that up to this point there have been significant differences between the characterisations that have been developed for global education and citizenship education. These differences are revealed through an examination of three areas: focus and origins; the attitude of the government and significant others; and the adoption of pedagogical approaches. We suggest that it would be useful to look beyond old barriers that have separated citizenship education and global education and to form a new global citizenship education. Their separation has in the past only perpetuated the old understandings of citizenship and constructed a constrained view of global education.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.083 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.008 | 0.013 |
| 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".