Supporting decision-making in retirementplanning: do diagrams on pension benefitstatements help? ESRI Research Bulletin 2019/09
Bibliographic record
Abstract
This study used a controlled experiment to test whether explanatory diagrams can improve comprehension of pensions and increase willingness to contribute to a pension. The study was undertaken by the ESRI’s Behavioural Research Unit in collaboration with the Pensions Authority. Central Statistics Office (CSO) figures show that just 36% of 25-34 year-olds in employment have a pension plan. Even workers who do have a plan face replacement rates (income after retirement as a proportion of income before retirement) well below the recommended 70%. International research suggests that failure to understand how pensions work contributes to this picture. Meanwhile, evidence from educational psychology shows that, across multiple areas of learning, comprehension can be improved by diagrams. We therefore tested whether the inclusion of explanatory diagrams on a pension benefit statement (PBS) could, first, improve understanding of how pensions work and, second, increase willingness to contribute to a pension.
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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".