From Funding Equity Initiatives to Research Productivity: Quantifying the Impact of NSF ADVANCE Awards on Recipients’ Publication Trajectories
Bibliographic record
Abstract
Service work in academia, including organizational change efforts, often competes with time for research, potentially affecting academic careers (tenure, promotion, and pay) through slowed publication productivity. However, little is known about how involvement in such efforts affects publication strategies or whether external funding mitigates the potentially negative impacts on research activity. The authors examine changes in publication trajectories among academics participating in the National Science Foundation ADVANCE program, an externally funded gender equity initiative. Using bibliometric data and a matched sample, the authors find that scholars involved in ADVANCE awards published significantly more articles within the first four years after receiving funding. This increase cannot be fully attributed to shifts in research focus, such as publications on gender, or changes in collaboration patterns. Instead, ADVANCE resources created a spillover effect, boosting publications in gender equity while also enhancing productivity in scholars’ primary research areas. These findings suggest that external and institutional resource allocation can offset the additional burdens associated with organizational change work, enabling academics to maintain active research careers while contributing to sustainable change initiatives. This highlights the critical role of robust resource provision in supporting faculty members engaged in organizational change.
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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 | 0.034 | 0.169 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".