A Brief Report on the Canadian Chapter: Signing of the MoU between UiTM and the University of Ottawa, Canada. / PM Khas Dr Angeline Ranjethamoney Vijayarajoo
Bibliographic record
Abstract
This memorable and historical journey began on the 15th of August, 2022. The members of the team who left for Canada on government service comprised the following people: Prof Yamin Yasin (our Rector, UiTMCNS) Ts Dr Noorlis Ahmad (Deputy Rector, Academic Affairs, UiTMCNS) Dr Siti Nor Atika Baharin (Liaison Officer, UiTM Global, UiTMCNS) Associate Professor Dr Angeline Ranjethamoney Vijayarajoo (UiTMCNS, APB Seremban,) We began our journey from Kuala Lumpur to Canada, with a transit stop- over at Doha. After this, our first point of entry to Canada was Montreal, where we had a connecting flight to Toronto. Toronto was where our first official duties began on the 15th of August, 2022. However, the focus of this article is the signing of the MoU between the University of Ottawa and Universiti Teknologi MARA, Malaysia. Hence, this article will only focus on the MoU Ceremony between Universiti Teknologi MARA (UiTM) and the University of Ottawa (UO).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.028 | 0.004 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.051 | 0.006 |
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".