Filipino youth professionals in Alberta, Canada: shaping gender and education landscapes?
Bibliographic record
Abstract
This chapter adopts Critical Theory in introducing and examining the narratives of Filipino youth professionals in Alberta, Canada as reflective of the interplay of age, gender, and migration that influences or is influenced by the neoliberal policy regime of internationalization of education that, in turn, gives rise to instances of decredentialing and recredentialing within spatial (i.e., household and state) and power (patriarchy, ethnicity, capitalism) logics of both home and host countries. In particular, these narratives focus on Filipino youth in relation to their choice of upholding their basic human right of international mobility for the good life and their perceived educational preparedness in migrating to Alberta, Canada. The narratives uncover the nature of Alberta’s international education framework as procuring profit from Filipino youth professionals who end up as workers first and professionals second. Filipino youth professionals’ credentialing journey takes on the dynamics of the power logics of ethnicity and capitalism operating within the spatial logics of the household and the state that result in Canada’s dominance over their lives and exploitation of their creativity and labor. This chapter concludes with the call for Canada to review and reformulate Canadian (Albertan) policies as they relate to foreign-trained qualifications.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.040 | 0.015 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".