Public Impact-Focused Research Survey Results
Bibliographic record
Abstract
The Association of Public and Land Grant Universities (APLU) Council on Research (COR) led an initiative to define, identify, and develop a recommended path forward for public impact research (PIR). A survey was conducted of APLU institution in order to: To characterize the extent of public impact research (PIR) occurring at APLU institutions. To understand how institutions (or leaders within institutions) think about, define, and communicate about this type of work. To provide perspectives about the challenges, opportunities, and rewards that may be associated with this type of scholarship. Responses were received from a diverse set of seventy public and land grant universities (APLU total membership was 239 universities at the time of this survey). Research expenditures at responding institutions ranged from $5 million to over $1 billion in FY 2017, and respondents included Hispanic-serving institutions, historically black universities, IEP-designated universities, and were received from 26 US states and one Canadian province. This document contains the complete set of de-identified responses to the survey. The intent is to make this broadly available and accessible to individuals or groups who may want to further analyze or use these results.
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 | 0.023 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
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".