Bleak expectations: the ongoing financial impact of the pandemic:Findings from the 5th Coronavirus Financial Impact Tracker Survey
Bibliographic record
Abstract
abrdn Financial Fairness Trust | Bleak Expectations 1 abrdn Financial Fairness Trust has commissioned YouGov to conduct a periodic cross-sectional tracker survey on the financial impact of the coronavirus pandemic across the UK.The first four surveys were conducted in April, May and July 2020 and March 2021.The fifth -the findings of which are presented here -was conducted in October 2021.The findings are based on responses from 5,770 individuals about their income, payment of bills, borrowing, debt, savings and ability to pay for other essentials such as food.A team from the University of Bristol analysed the data and produced these findings. OVERVIEWIn October 2021, while four in ten UK households (38%, 10.5 million households) enjoyed high levels of financial wellbeing and were financially secure, more than a quarter (27%, 7 million households) were either struggling to manage (4 million households) or in serious financial difficulties (3 million households).This picture was largely unchanged from April 2020.However, even most financially secure UK households said they were having to spend more because of the rising costs of essentials, highlighting the cost of living crisis facing UK households coming in winter 2021.This fifth edition of the Tracker also took stock of how UK households have fared financially over the 18 months of the pandemic to October 2021.The data confirms that the pandemic has exacerbated the financial resilience gap that already existed prior to March 2020.We found that for every household that saw their financial situation get a little or a lot better (21% of all UK households), two households saw their financial situation get a little or a lot worse (38%).The data suggests that the financial resilience gap could widen even more between October and December 2021, polarising UK households further according to those who have fared worst and best over the course of the pandemic.Among households whose financial situation had already deteriorated substantially, the financial outlook was estimated to be poor or quite poor for 72% of them.The outlook was similarly bleak for single parent households (with 65% estimated to have poor or quite poor financial prospects); those with a disabled householder (66%); households receiving Universal Credit (83%); and workless households receiving Universal Credit (91%).
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".