A Job Training Report As A Course Consultant At EF English First Jemursari Surabaya From 23 January 2015 Until 26 Febbruary 2015
Bibliographic record
Abstract
In the writer’s opinion, having an internship at EF English First was a really worthy experience. The writer learned a lot from those experiences. After finish his internship at EF English First the writer had made so much improvement especially in communication skill and marketing. He met a lot of people with many characteristics as his customer at EF English First. During the internship, the writer not only learned about EF product knowledge, but also how to communicate with other people by talking to customer or the other staff in EF English First. He also experienced talking English formally in the office. \nThe writer realized that nowdays English is very essential thing in this world. It was experienced by the writer when he had his internship. Working in an English school made him would be able to speak in English in some situations because some of his working parents and the teacher are from another country. He also had a lot of relations not only from Indonesia, but also from outside Indonesia such as Australia, Canada, USA,and United Kingdom. By the internship, the writer got his full-time job as a course \nconsultant at EF English First Klampis. It is an honor for the writer to be a part of English First. \nThe writer assume that EF English First is a suitable place to have an internship for English Diploma major. The writer definitely suggets his juniors in college to have \ntheir internship at EF English First. By having an internship at EF English First, the writer can apply the subjects that he can learned at college. It was a great experiences that the writer might not get in other internship places. Due to its great achievement in facilitating the writer in doing his internship, he hopes that EF English First will cooperate with English Diploma Program Universitas Airlangga in terms of the internship in the upcoming years.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.114 | 0.023 |
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".