Bibliographic record
Abstract
WHILE MANY HIGH SCHOOL STUDENTS like Anna confess a desire to develop personal money management skills, statistics tracking the average Canadian’s personal debt underscore the need to ensure our youth have the tools they need for financial success. What would it take to motivate teens to learn more about how they spend and manage their money? Assuming Anna’s experience is typical, a new approach was needed to capture the attention of Canadian youth at this critical juncture in their lives. Frustrated and dissatisfied with the haphazard and unfocused curriculum development in the field, we finally asked, ‘in what ways are our youth engaging in learning outside of school, and how might we tap into that momentum?’ The resource that emerged, ATM Confessions: A Financial Literacy Library, sprang organically from the interactions, discussions, and debates of a research and development partnership between The Investor Education Fund (IEF) and the Faculty of Education at The University of Western Ontario (UWO) over a five-year period. Positioning students and teachers as collaborators at the center of the research and development process allowed us to get to the heart of what would engage the populations we were seeking to help. Anna’s comments are fairly representative of her peer group in our research: acknowledgement that they should pay more attention to the flow of money, concern for the dangers of overextension and mismanagement of credit, and a desire for more understanding of the basic concepts of saving, budgeting, and investing. In Anna’s case, although she has been introduced to some concepts in school and at home, she remains disconnected from any authentic application of the knowledge and skills in her own life. KATHY HIBBERT AND ELIZABETH COULSON
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.090 | 0.022 |
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".