Status of Consumer Education and Financial Education in Canada (2016)
Bibliographic record
Abstract
This article reports on the status of consumer education in Canada in 2016 (junior and senior high), relative to the 2015-initiated federal national financial education/literacy strategy. Questions addressed: (1) Is it necessary to have separate financial education curricula when consumer education is available? and (2) Are the existing consumer education curricula adequate? After conceptualizing consumer education and literacy relative to financial education and literacy, a content analysis of provincial and territorial education documents identified 64 courses containing consumer-related content in seven subject areas. The majority (73%) of the 216 instances of consumer-related content—mostly (68%) found in home economics/family studies, social studies, mathematics, and business—pertained to resource management, with equal coverage for each of citizen participation (14%) and decision making (13%). Most (73%) of the courses were not offered until senior high. Results confirmed a fragmented and inconsistent approach to consumer education across subject areas, grade levels, provinces/territories, and regions. To stimulate dialogue, the national financial education strategy is framed as a stop-gap measure until there is political will for a pan-Canadian consumer education curriculum, predicated on the assumption that consumer education (not financial education) better prepares citizens for any future global depression. Appendix: McGregor Appendix
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".