Auditing Canadian Curricula for the Prevalence of Personal-Finance Related Terms Using Text-Frequency and Distant-Reading Software Tools
Bibliographic record
Abstract
This quantitative research study explores each set of provincial curriculum documents (save for Quebec) for the frequency of user-selected personal-finance based terms. The data from each province is compared and examined to answer: which provincial curriculum mentions user-selected personal-finance terms most frequently; how many words/pages are between each term mention, and how many personal-finance terms appear in the top 500 words in each curriculum. The research found that Prince Edward Island’s curriculum documents contained the most searched-for terms (at least one mention). Further, money (British Columbia and Newfoundland) and entrepreneur (Nova Scotia) are the only two searched-for personal-finance terms among the 500 most frequently-mentioned words in each set of curriculum-based corpora and appear approximately once every 16.5 pages in each of those respective sets of curriculum documents. This project uncovers a wealth of information about the extent to which personal-finance related terms appear in each province's curricula via software-based tools. However, further research is encouraged to corroborate these findings. Furthermore, follow-up classroom-based observations would provide useful qualitative evidence to triangulate the quantitative data and enhance the inquiry into the state of personal-finance education in Canadian schools. Moreover, those interested in utilizing the data sets from this project in future research need to be aware that text-based term mentions are not necessarily indicative of in-class practices. Thus, further research needs to be conducted to gain a deeper understanding of school-based personal-finance learning, and complementary projects examining the impact of text mentions on human behaviour are encouraged.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.013 | 0.020 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".