Understanding the disparity in financial capability across individuals' social locations: A new dimension of inequality
Bibliographic record
Abstract
In recent years, social work scholars have focused on financial capability to help individuals and families experiencing increasing disparities in wealth and income. Although a number of studies have examined the association between individuals' levels of financial knowledge and their financial behaviour and practices, we know little about how financial knowledge varies across individuals living in different social locations, including age, gender, and income statuses. In this dissertation, I used quantitative data from the 2009 and 2014 Canadian Financial Capability Survey and qualitative data collected from key informants to examine the disparity in financial knowledge and confidence and to identify groups that are disadvantaged. Findings suggest that older adults significantly overestimate their levels of financial knowledge, which implies vulnerability to financial fraud, exploitation, and abuse. Findings also suggest that individuals living in low-income have significantly lower levels of financial knowledge than their non-low-income counterparts. But no gender gap in financial knowledge was found within both low-income and non-low-income groups. The findings of the qualitative inquiry complemented the findings of the quantitative studies. In addition, the findings of the qualitative inquiry suggest that financial knowledge is contextual and driven by financial practices. Findings also suggest that financial knowledge alone is not sufficient to make informed financial decisions; individuals need financial confidence, access to financial products and services, and sufficient income to achieve financial wellbeing.Building financial knowledge among vulnerable groups such as older adults and those in poverty needs to be a policy and practice priority. Because social workers work closely with vulnerable and disadvantaged groups, they can help older adults, and individuals and families in poverty enhance financial wellbeing through building awareness, financial knowledge, and confidence and advocate for policies.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".