Digitalisation and the welfare state – how First Nations people experienced digitalised social security under the Cashless Debit Card
Bibliographic record
Abstract
Digitalisation of the welfare state has intensified in recent years, with burdens unevenly distributed between technology advocates and those receiving government income support. Putting in place processes where people needing social security must meet mandatory requirements of digital literacy and divert a significant amount of their small incomes to pay for expensive technologies such as computers, smartphones, and data plans comes at a cost. This article examines lived experiences of First Nations Cashless Debit Card (CDC) holders who experienced digitalisation of their social security payments. Under the CDC, a range of restrictions were placed on purchases, spending social security income came with stigma, technology troubles meant that income was less secure, and Indigenous peoples’ autonomy was undermined. Although the CDC has since been abolished, these issues remain relevant as a new cashless social security card, the SmartCard, has been introduced in 2023.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.019 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".