Beyond Academia: A case for reviews of gray literature for science-policy processes and applied research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Gray literature is increasingly considered to complement evidence and knowledge from peer-reviewed literature for science-policy processes and applied research. On the one hand, science-policy assessments need to consider a diversity of worldviews, knowledge types and values from a variety of sectors and actor groups, and synthesize policy-relevant findings that are salient, legitimate and credible. On the other hand, practitioners and scholars conducting applied research are affected by the time lag and biases of academic publication processes. Gray literature holds diverse perspectives informative for science-policy processes as well as practical evidence unfiltered by commercial publication processes. However, its heterogeneity has made it challenging to access through conventional means for a literature review. This paper details one endeavor within the Values Assessment of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) to review gray literature using Google’s Programmable Search Engine. In the absence of a standardized approach, we build on the limited experiential knowledge base for reviewing gray literature and report on the potential applicability of our strategy for future reviews. Gray literature review results contrast findings of our parallel review of academic literature, underlining the importance of mobilizing different knowledge bases in science-policy assessments, evidence-based practices, and applied research.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.058 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.060 | 0.240 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it