Adaptasi filem 'Cool Runnings' dalam melatih pemanah dan pelajar UiTM Pahang / Mohamad Azmi Nias Ahmad and Mohd Aripin Amat
Bibliographic record
Abstract
Di awal penubuhan Kelab memanah UiTM Pahang, suntikan semangat yang berkesan amat perlu bagi mereka terus berjuang dan mencapai matlamat yang telah ditetapkan. Filem 'Cool Runnings' telah digunapakai oleh jurulatih bagi membangkitkan semangat supaya tidak mudah berpulus asa, berfikiran terbuka dan global dengan hanya menggunakan sumber yang sedikit. Ianya terbukti berkesan apabila pemanah kelab ini telah berjaya membantu Pahang memenangi pingat Emas SUKMA pertama setelah 10 tahun kemarau pingat di SUKMA 2008 dan memecahkan rekod SUKMA 2010. Semangat yang ditunjukkan oleh empat atlet daripada Jamaica di dalam filem ini perlu dicontohi bukan hanya kepada atlet UiTM Pahang sahaja, tetapi juga oleh seluruh pelajar UiTM bagi mencapai Misi, Visi dan Objektif UiTM (Univesiti Teknologi MARA, 2010). Selain daripada itu, kertas ini akan membincangkan bagaimana nilai yang di tunjukkan di dalam filem ini menepati kesemua tujuh Model Graduan UiTM.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.052 | 0.019 |
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".