The global corporate curriculum and the young cyberflâneur as global citizen
Bibliographic record
Abstract
\n\t\t\t\t\tContents: Introduction: youth, mobility, and identity / Nadine Dolby and Fazal Rizvi -- New times, new identities -- The global corporate curriculum and the young cyberfleneur as global citizen / Jane Kenway and Elizabeth Bullen -- Shoot the elephant: antagonistic identities, neo-marxist nostalgia, and the remorselessly vanishing past / Cameron McCarthy and Jennifer Logue -- New textual worlds: young people and computer games / Catherine Beavis -- Diasporic youth: rethinking borders and boundaries in the new modernity -- Consuming difference: stylish hybridity, diasporic identity, and the politics of culture / Michael Giardina -- Diasporan moves: African Canadian youth and identity formation / Jennifer Kelly -- Popular culture and recognition: narratives of youth and Latinidad / Angharad Valdivia -- Mobile students in liquid modernity: negotiating the politics of transnational identities / Parlo Singh and Catherine Doherty -- Youth and the global context: transforming us where we live -- The children of liberalization: youth agency and globalization in India / Ritty Lukose -- Youth cultures of consumption in Johannesburg / Sarah Nuttall -- Identities for neoliberal times: constructing enterprising selves in an American suburb / Peter Demerath and Jill Lynch -- Disciplining "Generation M": the paradox of creating a "local" national identity in an era of "global" flows / Aaron Koh -- Marginalization, identity formation, and empowerment: youth's struggles for self and social justice / David Quijada.\n\t\t\t\t
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.029 | 0.002 |
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".