Bibliographic record
Abstract
Learning Objectives You will recall from Chapter 3 that for most people during most of their lives, human capital is the most valuable asset on their personal balance sheets. Have you ever thought about what asset is next in line – what your second most valuable asset is? It probably would not surprise you to learn that for homeowners, this spot is occupied by their personal residences. Although home-ownership rates (as percentages of households) vary from one country to the next, they currently exceed 60% in many countries such as the United States, Canada, Great Britain, Singapore, and Spain. For homeowners, the value of their houses represents a sizable portion of their total assets. In this chapter, we examine housing decisions. Specifically, you will learn how to decide between buying and renting a house. Buying or Renting? The decision between buying and renting a house is a difficult one because it depends not only on financial considerations, but also on lifestyle choices and personal circumstances. We will start from a purely financial perspective. From a Purely Financial Perspective To make a decision based strictly on financial considerations, you need to consider the cost and payoff of each alternative. If you rent, the only expenses that you have are the rental expense and tenant's insurance expense. Tenant's insurance covers your possessions (which can be lost due to theft, fire, or water damage) and liability from any damage that you cause to your building or other people who live there. Both of these expenses are recurring monthly expenses that you have to pay as long as you rent the property.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.090 | 0.022 |
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".