A Critical Reflection of the Disturbing Underrepresentation of Filipinos in Higher Education
Bibliographic record
Abstract
Filipinos are one of the largest visible minority groups in Canada and, each year, the rate of immigration into the country continues to increase. According to the 2010 Statistics Canada report, the Philippines was the number one source of immigrants, surpassing both India and China, which usually rank first due to their large populations (Chagnon, 2013). Despite the growing presence of Filipinos in Canada and their unwavering contributions to the economy, Filipinos remain disturbingly underrepresented in university institutions and higher education. Growing bodies of literature are now recognizing the issues and systemic barriers that disproportionately impact Filipino immigrants and the factors impeding Filipino youths from accessing a university education (Mendoza, 2018). The government of Canada claims that, as the leading multicultural country in the world, the country has successfully established spaces and opportunities for equity, inclusion, and upward mobility for immigrants (Brosseau & Dewing, 2009, Revised 2018). If this is indeed the case, then such a promise must be nourished to ensure that all racialized and marginalized groups are included and benefit from this pathway. Any gaps or issues must, therefore, be examined and addressed. This critical reflective paper will analyze significant events that I have personally experienced and which have heavily affected my decision to pursue post-secondary education. The main focus will be on the lack of role models, inadequate networks and connections, financial instabilities, and lack of parental guidance as possible reasons for the underrepresentation of Filipinos in university institutions.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.033 | 0.025 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.011 | 0.037 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".