A comparative study of Quebec & Ontario's curricula in financial literacy education: presence, components and aims
Bibliographic record
Abstract
Following 2008, the global financial crises, a lot of scholars and educators questioned the presence of education in teaching financial literacy (FL) to citizens.Since then, in Canada, a lot of work has been done to promote financial literacy education (FLE) in K-12.In Quebec and Ontario also the concepts of FL were developed in the curricula.In this research, I intended to study the presence of FLE in Canadian school curricula.For an in-depth study, I applied qualitative comparative case study method, with a systematic approach.I collected the units of my data from provincial curriculum documents.I also narrowed down my investigation into studying the FL components and aims in Quebec and Ontario's curricula.I explored the similarities and differences of these two curricula in secondary five (grade 11) for Quebec and secondary 12 for Ontario (high school level) regarding FL.I conducted my study within the lenses of my conceptual framework, which I formed it based on conceptual blending model (Fauconnier & Turner (2003), and I called it Financial Competency for Students (FCS).FCS assisted me in analyzing and comparing the FL learning components in each province.Such analysis and comparisons showed which province in its curriculum has explicitly or implicitly addressed most of the defined concepts in FCS.The findings also showed both provinces have official curriculum documents specifically named Financial Education (Quebec), and Financial Literacy Scope and Sequence of Expectations (Ontario).In Quebec, Financial Education (as a single official document) was integrated into Social Sciences in secondary five (Grade 11).In Ontario, Financial Literacy Scope and Sequence of Expectations (as a single official document), integrated into broader areas of study such as Social Sciences and Humanities, Mathematics,Table of Contents
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".