From Intent to Impact-The Decline of Broader Impacts throughout an NSF Project Life-Cycle
Bibliographic record
Abstract
Our findings indicate a systematic decline in impacts from the proposal stage, abstracts, to the conclusion of the project, project outcome report. Impacts decline in all but four categories (13 of 17) and all but two directorates (5 out of 7). Across the sample, impacts decline by 14%, but the decline is most pronounced when the impacts are intrinsic to the research or targeted at marginalized groups. This finding is troublesome given the NSF’s commitment to broaden participation by engaging underrepresented groups. Grants with inclusive impacts have less funding and prove more difficult to achieve. By contrast, grants report more impact for advantaged groups from the abstract to the POR. From this finding, it appears the interpretation and enforcement of BI policy is not currently serving marginalized groups. This lack not only maintains the status quo, but may hinder the development of scientific thought due to an absence of diversity society. Impacts that serve the general population suffer less attrition than inclusive impacts, but these still make up less than a quarter of impacts achieved by the end of the research period.
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 | 0.047 | 0.147 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".