Assessing policy and practice impacts of social science research: the application of the Payback Framework to assess the Future of Work programme
Bibliographic record
Abstract
The UK Economic and Social Research Council funded exploratory evaluation studies to assess the wider impacts on society of various examples of its research. The Payback Framework is a conceptual approach previously used to evaluate impacts from health research. We tested its applicability to social sciences by using an adapted version to assess the impacts of the Future of Work (FoW) programme. We undertook key informant interviews, a programme-wide survey, user interviews and four case studies of selected projects. The FoW programme had significant impacts on knowledge, research and career development. While some principal investigators (PIs) could identify specific impacts of their research, PIs generally thought they had influenced policy in an incremental way and informed the policy debate. The study suggests progress can be made in applying an adapted version of the framework to the social sciences. However, some impacts may be inaccessible to evaluation, and some evaluations may occur too early or too late to capture the impact of research on a constantly changing policy environment.
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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 | 0.418 | 0.414 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.028 | 0.015 |
| Science and technology studies | 0.007 | 0.034 |
| Scholarly communication | 0.023 | 0.023 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.007 | 0.006 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".