Debts, Loans, and Mortgages [Canadian Content]
Bibliographic record
Abstract
Learning Objectives In this chapter, we cover various issues concerning usage of debt. In particular, we look at how mortgage financing works. We then talk about the advantages and shortcomings of investment loans (i.e., borrowing money to invest). Finally, we discuss the benefits of debt consolidation (i.e., combining debts of various sources into one). Mortgage Financing Suppose you want to buy a house. After visiting several potential choices, you decide on one house that you like the most. You then negotiate with the seller on the price. Now, you have to decide how much ofthe payment will come from your own money (i.e., down payment) and how much from borrowing. In many countries, there is a rule governing the minimum down payment that a buyer needs to make. Typically, the minimum is expressed as a percentage of the house's price or appraised value. For Canada, that minimum is 5% (as of early 2011). Obviously, you can make a larger down payment than the minimum if you want. Depending on the amount of your down payment, your mortgage can be classified as conventional or high-ratio. A conventional mortgage is one where the buyer makes a down payment of at least 20% of the purchase price and borrow the rest (or, equivalently, the loan-to-value ratio is 80% or below). On the other hand, a high-ratio mortgage is one where the down payment is between 5% and 20% (or the loan-to-value ratio between 95% and 80%).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.057 | 0.013 |
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".