Experience and insight: Canada-ASEAN exchanges program
Bibliographic record
Abstract
Ts. Hafizah is a senior lecturer at UiTM Pulau Pinang and is currently taking a break from teaching to focus on her PhD studies. While working on her degree, she received the Canada-ASEAN Scholarship and Educational Exchanges for Development (SEED) from the Canadian Bureau for International Education. This awesome scholarship allowed her to spend six months as a Visiting Research Student (VRS) at the University of Ottawa, Canada, from January to June 2024. This program, offered every year by the Government of Canada, provides students from ASEAN member countries with the opportunity to engage in short-term study or research at Canadian institutions. The scholarships and educational exchanges aim to support the achievement of all sustainable development goals (SDGs) and align with the SEED goals which is to strengthening people-to-people ties between Canada and the Indo-Pacific region. Therefore, to qualify for the scholarship, students must apply and meet certain requirements, including having their local university partnered with the Canadian institution where they wish to study or conduct research. Luckily, in 2022, our Vice-Chancellor at UiTM established a strategic partnership with the University of Ottawa, making this scholarship opportunity possible! Ts. Hafizah really made the most of her time during the attachment program, soaking up all the knowledge and experience she could from the university and the area around it. She was guided by Prof. Dr. Ghasan Doudak from the Department of Civil Engineering at the University of Ottawa, who helped her with her research. With his support, she was able to test timber beams and take advantage of the university's top-notch equipment and facilities. She also got the chance to attend the 2024 Ottawa Wood Solutions Conference organized by the Canadian Wood Council (CWC). It was a great way for her to meet new people in her field and pick up some cool insights about timber structure research. A bunch of pros from the timber industry, including university experts and folks from different sectors, were there.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.045 | 0.008 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.030 | 0.003 |
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".