Broader Impacts Evaluation: A Physics-Inspired Framework for STEM Impact
Bibliographic record
Abstract
Quantifying the broader societal impacts of STEM research remains elusive; most evaluations stop at participant counts or satisfaction surveys, obscuring the magnitude and mechanics of change. The SDD (Scale, Depth, Duration) Framework offers a physics-inspired solution by mapping the base units of impact force: mass to scale, distance to depth, and time to duration, thereby enabling calculation of derived metrics—velocity (rate of change), momentum (program sustainability), acceleration (change in velocity), force (transformative power), and work (impact efficiency). This pre-print outlines the framework's theoretical foundation and demonstrates its application to an outreach event, representing one of four broader impacts domains: informal science communication, higher education and professional development, broadening participation in STEM, and innovation and industry partnerships. The activity engaged 90 participants, averaged 7 min per group of five, and produced a 50.9% shift from observational to mechanistic explanations of material properties. Velocity calculations (v = Δx/Δt) indicated 7.3 percentage points of conceptual development per minute (reported as an SDD Impact Statement). These standardized metrics support data-driven optimization and cross-program comparison across all broader impacts categories, providing scientists with a rigorous, familiar language for evidence-based decision-making in broader impacts practice.
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 | 0.081 | 0.102 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.017 | 0.007 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".