Succeeding as an International Student in the United States and Canada
Bibliographic record
Abstract
Each year, 700,000 students from around the world come to the United States and Canada to study. For many, the experience is as challenging as it is exciting. Far from home, they must adapt to a new culture, new university system, and, in many cases, a new language. The process can be overwhelming, but as Charles Lipson's as an International Student in the United States and Canada assures us, it doesn't have to be.Succeeding is designed to help students navigate the myriad issues they will encounter - from picking a program to landing a campus job. Based on Lipson's work with international students as well as extensive interviews with faculty and advisers, Succeeding includes practical suggestions for learning English, participating in class, and meeting with instructors. In addition it explains the rules of academic honesty as they are understood in U.S. and Canadian universities.Life beyond the classroom is also covered, with handy sections on living on or off campus, obtaining a driver's license, setting up a bank account, and more. The comprehensive glossary addresses both academic terms and phrases heard while shopping or visiting a doctor. There is even a chapter on the academic calendar and holidays in the United States and Canada.Coming to a new country to study should be an exciting venture, not a baffling ordeal. Now, with this trustworthy resource, international students have all the practical information they need to succeed, in and out of the classroom.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.047 | 0.006 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".