Beyond Academia: A case for reviews of gray literature for science-policy processes and applied research
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 both 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, especially in environmental and health-related fields, are affected by the time lag and documented biases of academic publication processes. While gray literature holds diverse perspectives that need to be integrated in science-policy processes as well as practical evidence unfiltered by commercial publication processes, 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 a limited experiential knowledge base for reviewing gray literature and report on the potential applicability of our strategy for future reviews. Our results contrast the 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.
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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 | 0.354 | 0.619 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.039 | 0.031 |
| Science and technology studies | 0.008 | 0.031 |
| Scholarly communication | 0.041 | 0.050 |
| Open science | 0.006 | 0.022 |
| Research integrity | 0.014 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".