Imagined Liberation: Xenophobia, Citizenship and Identity in South Africa, Germany and Canada
Bibliographic record
Abstract
ForewordAcknowledgments List of Abbreviations and AcronymsIntroductionPart I Integrating Difference1 Comparative Xenophobia2 South African Perspectives on Xenophobia3 Youth VoicesAim and Methodology ? An Ethnography of Township Schools ? How Students View Foreigners4 Falling from Grace Shifting Views on Mandelaland ? Reflections on Mandela ? Patriarchy, Sexual Violence, and HIV/AIDS ? Crime and Punishment ? Corruption and Consumption? Reracialization, Affirmative Action, and Black Economic Empowerment ? Descent into Zimbabwe? ? Popular Sentiment versus a Liberal ConstitutionPart II Variations of Migration Policies: Africa, Germany, and Canada5 Settler ColonialismTwo Types of Colonialism ? Founding Myths and Intergroup Attitudes ? Metropolitan/Settler Relations6 Xenophobia in GermanyThe Case of Roma/Sinti ? Muslims as Enemies ? Capitalist versus Communist Xenophobia ? Conclusion7 Multicultural Canada as an Alternative?Canadian Identities and Cultural Traditions ? How to Select Immigrants ? Opportunistic MulticulturalismPart III Political Literacy8 Xenophobia and Political Literacy Comparing Political Education in Multiethnic Societies ? Political Literacy as Strategy to Combat Xenophobia ? Nation, Nationalism, Ethnicity, Ethnocentrism, and Critical Patriotism ? Cosmopolitan Consciousness9 Theorizing XenophobiaConclusion: Alternatives and Global TrendsAppendicesAutobiography I: Navigating Difference: Insiders, Outsiders, andContending Identities (Kogila Moodley) Autobiography II: Controversies: Peacemaking in Divided Societies(Heribert Adam) ReferencesIndex of Names
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.024 | 0.008 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".