The Perfect Storm: Living on Universal Credit during the Cost of Living Crisis
Bibliographic record
Abstract
The last few years have been extraordinarily tough. The pandemic left 1.8 million people in Scotland financially worse off, and even before the most recent increase in the energy price cap one in three people found their bills unaffordable. Now people are faced with a perfect storm of soaring prices and flat or falling incomes, which risks sweeping tens of thousands of people across the country into poverty, problem debt, and destitution. \n \nThose relying on the social security system are particularly vulnerable to poverty. Just over 447,500 people across Scotland are on UC – equivalent to more than one in ten working age adults in Scotland and almost double the number before the pandemic. Getting social security right is vital to help these people weather the storm. \n \nRecent data from across the Citizens Advice network in Scotland shows the hardship people are facing every day: \n \nAdvice need for food banks has grown by almost a third (31%) since September 2021. \nAdvice need for other charitable support, including fuel bank referrals, saw a sharp increase of 23% between September 2021 and December 2021, likely reflecting the additional pressure of winter heating bills. \nAdvice on UC sanctions has grown by 53% over 2021-22. \nAdvice on UC Budgeting Advances has risen by 25% over 2021-22. \nAdvice on UC Overpayments nearly doubled from the average across 2020/21 to Q4 of 2021/22. \n \nBehind each of these statistics are real people. In this report we highlight four real Citizens Advice Bureau (CAB) client stories which show the incredible difficulties many people on UC are facing daily. Their names have been changed to protect their anonymity, but their stories demonstrate the reality of the cost of living crisis and the need for further support.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.039 | 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".