Digital for Good: A Global Study on Emerging Ways of Giving - United Kingdom
Bibliographic record
Abstract
The COVID-19 pandemic fundamentally altered many aspects of day-to-day life and the philanthropic sector in the United Kingdom (UK). Pandemic restrictions limited in-person interactions and accelerated an already growing digitalization of the UK philanthropic sector. However, past research found no conclusive evidence of the degree to which digital interactions will replace in-person fundraising. While 2020 witnessed a growth in online donations alongside a drop in cash donations, only a little more than a quarter of charities said digital fundraising was as effective as in-person fundraising.Key findings do affirm some pre-pandemic trends in giving methods in the UK. There was a marked increase in the proportion of people giving via website or app, which occurred at the same time as a decrease in donors giving via cash. Younger people donate online more than older adults, yet older age groups have also engaged more with online giving. On average, 60 percent of donors' gifts were made online in the 12 months prior to this study.Nevertheless, the findings also suggest that philanthropy will retain a human element. Most who used social media to request donations from family and friends also tended to make those requests in-person. And most British people expect that in the future we will give digitally rather than in cash, but almost half expected this to occur via in-person contactless donations tins.Overall, this report concludes that the post-pandemic fundraising landscape seems more likely to develop as a hybrid one, where online interactions complement—rather than substitute—offline interactions.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".