Digital Visualization of Transnationalism: Mapping Historical Migration of Hong Kong Migrants in Canada During the Handover of Hong Kong Period
Bibliographic record
Abstract
Human migration is a global phenomenon driven by two factors: individual needs or desires, and changes in the economics and politics in the society from which the migration stems. Human migration usually involves people crossing international borders, but the impact is often felt locally, in the economy, and in social, political, and cultural spheres. During the 1980s and 90s, Hong Kong Chinese went through a mass migration wave to western countries. The migration was strongest during the period of the handover of Hong Kong, and a primary destination was Canada. The purpose of this research is to map a migration wave from Hong Kong to Canada and back to Hong Kong during two crucial periods: 1984, when the handover decision was made; and 1997, the lead-up to when Britain handed political control of Hong Kong to China. The other purpose of this research is to gain insight into the factors that reflect these migration trends. The project involves digital technology, specifically a quantum geographic information system (QGIS), to capture the nodal Hong Kong migrant patterns in Canada by destination, gender, age group, and immigration class. The thesis will address neo-institutionalism; social identity; transnationalism; sense of place; the strong relationship between transnational space, changing institutions, cultural identity values and conflicts; and the transition of the affective sense of place that impacts the motivations and practices of Hong Kong Chinese migrants. This study is part of a body of research on transnational migration that crosses national borders, and uses large-scale data to visualize the relationship networks between the place of origin (Hong Kong) and destination (Canada), and spatial distribution of immigration into Canada.
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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 | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".