Cryptocurrencies are here to Stay! Think you aren’t Vulnerable, Think Again
Bibliographic record
Abstract
Cryptocurrencies are here to stay and represent a market capitalisation of approximately $3.57T. It has been reported that over 500 million people globally own cryptocurrencies. In this paper, I discuss the vulnerabilities identified with cryptocurrency ownership including who is vulnerable and then explore the role education plays. The study described involved 745 cryptocurrency owners (including 140 non-fungible token (NFT) owners) from Australia and explores the ways in which vulnerabilities play out for various demographics. The above-mentioned research revealed that almost everyone is vulnerable when it comes to cryptocurrency and/or NFT ownership and challenges who has previously been identified as experiencing financial vulnerabilities. Demographic characteristics associated with financial vulnerabilities often includes individuals with lower education levels who are living on low incomes, who identify as female and/or Indigenous and/or for whom English is a second language. It is often assumed that anyone else who does not meet the above-mentioned characteristics are assumed to be financially capable. However, with cryptocurrency ownership almost everyone is vulnerable. Education has a role to play to help combat the risks associated with cryptocurrency and/or NFT ownership. We need educators willing to teach about cryptocurrencies and NFT ownership, storage and tax implications.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 0.006 |
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".