research-document RFR-E0700D8F
Create a shared benchmark and decision threshold: Beautiful Digital Experiences Evidence Registry
RFR-E0700D8F — Create a shared benchmark and decision threshold: Beautiful Digital Experiences Evidence Registry
Research opportunity
Create a shared benchmark and decision threshold for the claims or recommendations in “Beautiful Digital Experiences Evidence Registry.”
Background
The originating artifact is accepted by the repository publishing inventory with status “active.” Its EV-BDE-006 — Pleasure–Interest model section provides the immediate evidence boundary.
Evidence trace
- Origin document: content/projects/beautiful-digital-experiences/evidence-registry/beautiful-digital-experiences-evidence-registry-v0-1.md
- Section:
EV-BDE-006 — Pleasure–Interest model - Specific assumption challenged: The source's treatment in “EV-BDE-006 — Pleasure–Interest model” is sufficiently supported for its intended scope.
- Supporting evidence excerpt: “- Source: Graf, L. K., & Landwehr, J. R. (2015). A dual-process perspective on fluency-based aesthetics: The pleasure-interest model of aesthetic liking . Personality and Social Psychology Review, 19(4), 395–410. <https://doi.org/10.1177/1088868315574978 - Type: theoretical synthesis. - Direct observation: the model d…”
- Reason this opportunity exists: How competing methods or implementations compare on a common corpus with explicit utility, safety, and cost thresholds.
Unknowns
- How competing methods or implementations compare on a common corpus with explicit utility, safety, and cost thresholds.
Dependencies
Suggested REP and methodology
- Suggested REP:
REP-EVREG-BDE-001-BENCHMARK - Methodology: Curate representative cases, blind ground truth where possible, define baselines and uncertainty-aware metrics, and run reproducible benchmark evaluations.
- Expected outputs: Versioned benchmark, baseline implementations, scoring harness, datasheet, and adoption decision rule.
- Success criteria: Independent teams can reproduce scores and the benchmark discriminates meaningful quality differences without rewarding proxy gaming.
- Recommended agent:
research-engineering-agent - Estimated effort: Medium
- Expected knowledge gained: How competing methods or implementations compare on a common corpus with explicit utility, safety, and cost thresholds.
Evaluation
| Dimension | Score (1–5) |
|---|---|
| Knowledge gain | 4 |
| Potential impact | 4 |
| Cross-project reuse | 5 |
| Scientific importance | 5 |
| Dependency cost | 5 |
| Implementation difficulty | 3 |
| Frontier score | 392 |
Confidence in this opportunity: moderate. Status: Open.