How Grey Literature Informs Policy and Decision Making: The Necessity to Understand \nthe Processes
Bibliographic record
Abstract
Effective advocacy for grey literature must be based on understanding the environments in which it is used. As advances in communications technologies continue to occur at seeming breath-taking pace, all forms of information are being affected. Evolving publication practices are presenting new communication opportunities, in addition to disruptions of established patterns, as long-standing genres are being reshaped by powerful technological and societal changes. Disruptions can cause discomfort and anxiety, but opportunities to promote the value of particular information genres also arise. Grey literature, for example continues to be produced in large quantities, which suggests that its importance in communication may be increasing rather than diminishing. Advocates of grey literature may believe this genre is undervalued or misunderstood, but lobbying for grey literature in the absence of understanding the contexts in which it is or can be used will likely fail unless information activity in those settings is understood. One prominent context encompasses public policy and decision making where grey literature is often present but typically not noticed. Policy and decision-making are notably complex processes and increasing attention is being placed on developing an understanding of the research-policy interface and evidence-based policy making in particular. Conferences (e.g., Science Advice to Governments, Auckland, New Zealand, August 2014), evidence information services (e.g., one launched in the United Kingdom in 2014), research programs and institutes (e.g., Environmental Information: Use and Influence, Dalhousie University), and other initiatives emphasize the importance of understanding the relationship between research and policy, a sometimes contentious and even dysfunctional activity. Drawing on findings from research conducted within the Environmental Information: Use and Influence research program, which involves governmental, intergovernmental, and non-governmental organizations, we outline roles for grey literature in policy and decision-making contexts. We note, for example, types of grey literature used in these contexts, we identify preferences for specific features of useable information by managers and policy makers, and we outline pathways of research evidence, some of which is produced as grey literature. Information use is a non-trivial phenomenon that must be understood in advance of advocating the value of grey literature.
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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 | 0.367 | 0.436 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.026 | 0.017 |
| Science and technology studies | 0.019 | 0.142 |
| Scholarly communication | 0.074 | 0.098 |
| Open science | 0.009 | 0.046 |
| Research integrity | 0.031 | 0.030 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".