Transnational social fields of the Yoruba in Toronto
Bibliographic record
Abstract
Until very recently, the word "immigrant" had evoked images of people who had come to stay, having been transplanted from their original home in order to make for themselves a new home to which they would pay allegiance. This thesis questions the assumptions that minorities and migrants demonstrate an exclusive loyalty to one nation-state. This is examined by exploring the mode of social connections and frequency by which one of the most salient African ethnic communities in Toronto, the Yoruba (the pedigrees of Oduduwa from the Federal Republic of Nigeria), maintain ties on various levels with their "home community" in this period of globalization. Yoruba migration is linked to their enmeshment in global capitalism, beginning with colonialism which extracted natural resources for the development of the European industrialism, and later neocolonialism which caused the pervasive penetration of global capital in the form of loans, and the collusion of internal social forces with the Western transnational corporations, leading to the pauperization of the mass of the Nigerian population. It is argued that the crass material exploitation of Nigeria, both in the colonial and postcolonial periods is not enough to explain the Yoruba migration and their transnational practices but should be viewed in conjunction with the "dependency complex" caused by the colonial and neocolonial domination of their "psyche".
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".