What does numeracy add? Exploring financial literacy, numeracy and other skills amongst low-income adults in Canada and the UK
Bibliographic record
Abstract
ABSTRACT There has been surprisingly little research about the role of numeracy in adult financial literacy, and the few existing studies available lead to contradictory findings. This paper takes a qualitative approach to explore the extent to which low-income adults in high-income economies use financial literacy and numeracy in their financial lives. Financial literacy is assessed in terms of three domains -- ‘keeping track’; ‘making ends meet’ and ‘staying informed’ -- via semi-structured interviews and background information on participants. Participants are also categorised into three groups based on discussion about their numeracy: ‘formally capable’, ‘informally capable’, and ‘uncomfortable’. Some participants exhibit elements of financial literacy but little or numeracy. There is no evidence that numeracy is essential, but having a variety of skills to draw on, potentially including numeracy, appears to be beneficial for low-income adults. The research contributes to the literature in two ways. It provides deeper understanding of the role of numeracy and also identifies two additional skillsets which appear to benefit low-income adults: digital skills and the ability to communicate clearly and advocate for oneself.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".