Understanding how math anxiety relates to financial illiteracy in Canada
Bibliographic record
Abstract
In the age of continuous innovation, Canadians face many challenges handling personal finances. Managing money is part of daily life, but financial products and services are more complex than ever before. This is why in 2015, the Government of Canada made financial literacy a priority by committing annual resources to the Financial Consumer Agency of Canada (FCAC) and in 2020, the Ontario Ministry of Education added financial literacy as a unique strand of math in the K-8 curriculum. All Canadians need a solid foundation of financial knowledge and skills to live within their means and feel in control of their financial lives. Before we can effectively improve financial literacy within this country, we need to better understand the cognitive and emotional factors that contribute to financial illiteracy and poor financial choices. Particularly, this study will provide insights into how negative emotions towards math (i.e., math anxiety) can adversely impact peoples' financial knowledge. Given the prevalence of math anxiety in Canadian society, it is crucial to gain insight into how math anxiety may contribute to financial illiteracy.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".