Bibliographic record
Abstract
The notion of financial literacy is not a new one. It is widely perceived as being important, hence something to be encouraged in those who are not financially literate, as exemplified by the existence of organisations dedicated to generating financial literacy in, for example, Australia, Canada, the U.K., and the U.S.A. But what does the term financial literacy actually mean? What distinguishes a financially literate individual from one who is financially illiterate? This chapter investigates aspects of financial literacy in particular contexts (embracing households and formal organisations – whether profitseeking enterprises or public sector bodies). As a prelude to defining what is meant by financial literacy, however, the basic idea of literacy itself (subsuming numeracy) is considered. The fraught confusion over financial awareness and financial literacy, often viewed as being synonymous expressions, is addressed. This confusion arises (at least in part) from inadequate definitions of financial literacy (which clearly has implications for its operationalisation). These limitations are explored and a fuller definition of financial literacy is provided as a point of reference for accounting educators
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".