Locating Diaspora: Afro-Caribbean Narratives of Migration and Settlement in Toronto, 1914–1929
Bibliographic record
Abstract
This article examines the experiences of Afro-Caribbeans in Toronto in the early twentieth century. It identifies and analyzes the practices and processes of diaspora at the local level and considers ways in which discourses of community, nation, and race travelled between sites and across borders. In so doing, it investigates the ways in which immigrant identities were constituted, contested, and reformulated in the tension between local experience and diasporic consciousness. As well, it evaluates how borders shaped the contours of trans-local and transnational communities. By extrapolating from individual histories, this article identifies several key features, institutions, processes, and practices that defined the Afro-Caribbean experience in Toronto and informed local engagements with global black and West Indian diasporas. These factors include encounters with discrimination, employment patterns, social relations, and organizations like Marcus Garvey’s Universal Negro Improvement Association. By “locating diaspora” in Toronto, this article elucidates the intersection and ongoing dialectics between the local and the global, and illustrates the significance of borders in shaping migration networks and constituting diasporic communities.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.018 | 0.009 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".