Arrival Survival Canada: A Handbook For New Immigrants
Bibliographic record
Abstract
1. Welcome to Canada Multiculturalism History of Canadian Immigration Overview of Canada 2. Canada's Major Cities Economy Climate and Transportation Places of Interest 3. What to Know Before You Go What to Bring With You Important Documents First Expenses 4. First Things First SIN Card Before Canadian Medical Coverage Starts Enrolling Children in School 5. Assistance Available English Classes Employment Insurance Financial Assistance 6. Medical Coverage Health Care System Finding a Doctor Prescription Medicine 7. Accommodation Finding a Place to Live Tenant Rights Buying a House or Condo 8. Smart Consumerism Paying for Housing, Utilities, and Transportation Shopping Tips Dressing for Canadian Weather 9. Banking, Credit, and Insurance Opening a Bank Account Getting Good Credit Buying Insurance 10. Looking for and Landing a Job Resumes Interviews Work Conditions 11. Education School for Children Adult Education Financial Assistance 12. Driving in Canada Getting a Driver's Licence Buying a Car Car Insurance 13. The Law Your Rights Legal Aid Canadian Courts 14. Income Taxes The Tax System Tax Credits Foreign Earnings 15. Customs and Etiquette Families Socializing Business Etiquette 16. Becoming a Citizen Applying for Citizenship Getting a Passport Voting
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.123 | 0.041 |
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".