Long Distance Transnationalism of Filipina Transmigrants in the Care Economy of Canada
Bibliographic record
Abstract
Long-distance transnationalism is defined and understand as a set of identity claims and practices that connect people living in various geographical locations to a specific territory that they see as their ancestral home (Schiller, 2005: 571-572). The actions taken by long-distance “transnationalists” on behalf of ancestral home may include the acts of voting, contributing money, demonstrating, lobbying, doing and creating works of art and other modes of connecting to ancestral home. In the case of Filipina transmigrants, long-distance nationalism is observed through contributing money to their respective families and being hailed as “bagong bayani” by saving countries GDP through dollar remittances. Therefore, the notion of long-distance transnationalism is closely connected to the classic notion of nationalism and the nation-state. As in other forms of nationalism, long-distant transnationalists believe there is a nation that consists of a people who share and connect with common history, identity, and territory in in various geographical locations. This means that it transcends the sense of nationalism beyond the borders of nationalism and of which seen as a notion of ‘long distance nationalism’, which refers to the ways the Filipina in the context of care economy in diaspora, exert influence from abroad while not bearing the consequences of their intervention in the homeland. This paper aims to answer the following: How do these consequences in the case of Filipinas in the care economy of Canada, affect or not affect their long distance transnationalism? How do they relate with the concept of “transnationalism” based on their experiences as Filipina transmigrants in Canada? What are these experiences of Filipina transmigrants in the ancestral home and host country and how this strengthen or weaken their sense of transnationalism? How do Filipina transmigrants in the care economy bear the consequences of being away from the home land during this global pandemic and how do they practice/perform long distance transnationalism?
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.026 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".