Financial Empowerment: Personal Finance for Indigenous and Non-Indigenous People
Bibliographic record
Abstract
Financial Empowerment is an adaptation of the openly licensed textbook Personal Finance, v. 1.0 which was adapted by Saylor Academy under a Creative Commons Attribution-NonCommercialShareAlike 3.0 License without attribution as requested by the work’s original creator or licensee and is available here: http://www.saylor.org/site/textbooks/. The purpose of this textbook adaptation is to take an accessible, student-focused personal finance textbook from the United States and make it affordable and relevant for Indigenous and non-Indigenous people in Canada. While many mainstream Canadian personal finance texts provide excellent content in terms of the mechanics of personal finance, they are expensive and not always relevant to the values and experiences of students in the classroom. Many mainstream personal finance texts fall short for Indigenous Canadians and non-Indigenous Canadians alike because they do not speak to readers’ varied backgrounds, knowledge systems, and experiences. This textbook aims to motivate a broad range of students to learn about personal finance. While it is beyond the scope of this book to address all of the diverse groups that comprise the Canadian population, the text does attempt to provide practical, student-focused information that all students can relate to.
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 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.003 |
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".