Celestica Electronics (M) Sdn Bhd / Diana Maisara Abdul Samad
Bibliographic record
Abstract
This report is prepared to fulfil the requirement of Bachelor of Business Administration (Hons.) International Business, Universiti Teknologi MARA (UiTM) Campus Bandaraya Melaka on internship at Celestica Electronics (M) Sdn. Bhd. Internship is a transition between study and working life. Therefore, choosing the right company to do an internship with is important. I have chosen Celestica Electronics (M) Sdn. Bhd. to perform my internship from 1 March 2023 until 15 August 2023. The company has a great influence in the manufacturing sector worldwide, as it produces the major portion of electrical appliances and collaborates with successful companies and has good competitive. Celestica Electronics company based in Canada has more than 23,000 employees all over the world. The focus of this study is to highlight and analyze the experience of doing an internship at Celestica. This study is divided into several sections. The first Chapter, Student's Profile consists of my latest resume. In Chapter 2, is about Company's Profile. There are introductions of Celestica, including the background, company mission and vision, business units, Celestica management and organizational chart. In Chapter 3, is about Training's Reflection. In this chapter, it contains my job and responsibilities during internship and also my personal accomplishments. For Chapter 4, it is about Celestica SWOT Analysis and discussion and recommendation. In the last chapter, which is Chapter 5, is about Conclusion.
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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.380 | 0.167 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".