Tracking the Influence of Grey Literature in Public Policy Contexts: The Necessity and Benefit of Interdisciplinary 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
Scientific information (much of it published as grey literature) can play a pivotal role in the search for solutions to serious global environmental problems. This fact is receiving growing attention by a diversity of researchers. How information functions within the interface between science and policy is only weakly understood, in part because most studies have been conducted through single disciplinary lenses. Moreover, determining the life cycles of scientific information and developing an understanding of the use and influence of this information are not trivial tasks. We believe that an appreciable increase in understanding can be achieved through an interdisciplinary perspective and a comparative approach employing a suite of research methodologies to document information pathways. In particular in our research (see www.eiui.ca), we contend that interdisciplinary research, drawing on “information science and management,” “marine environmental science,” “marine policy development,” “fisheries science and management,” and “public policy,” can substantially increase understanding of the processes by which scientific information is incorporated into environmental policy decisions. This innovative, evolving interdisciplinary perspective enables addressing the question “what role and influence does grey literature have in marine environmental policy and decision-making processes” in an informative, holistic manner, otherwise unfeasible. As this paper shows, multidimensional thinking and analysis stimulated by an interdisciplinary perspective is essential for understanding the role of scientific information at the science-policy interface in marine environmental fields.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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