Bibliographic record
Abstract
This book grew out of the deep frustration two of the three authors have experienced for many years, trying to teach a practical yet rigorous course on personal – in contrast to corporate or investment – finance, to undergraduate and graduate students at Canadian business schools. Although there are many college-level textbooks that discuss the tactical aspects of personal finance – nuggets such as: credit card debt is bad; reduce fees on mutual funds; regular saving is important; have a budget; and so on – we have not come across a textbook that integrated all these disparate concepts into a conceptual or strategic framework for financial decision making, based on sound economic principles . For the most part, personal finance is being taught as a collection of standalone facts about “smart” money management. Most existing textbooks are written assuming a (very) basic background in mathematics on the part of the student, which limits the financial and economic level at which such a course can be delivered and the material discussed. Moreover, in today's Google and YouTube world, curious students could obtain more relevant, accurate, and up-to-date information about most (if not all) of the products that are part of the personal financial toolkit. In our opinion, a textbook that allocates most of its pages to the tactical aspects of financial planning, such as explaining how to read a credit card statement or how to get a copy of your credit report from your local credit bureau, or how to open up a brokerage account, is not advanced enough for a third or fourth year course in a business school.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.252 | 0.114 |
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".