Assessing the Diffusion and Impact of Grey Literature Published by International Intergovernmental Scientific Groups: The Case of the Gulf of Maine Council on the Marine Environment
Bibliographic record
Abstract
Co-authored together with Ruth Cordes and Peter Wells. - Although many governmental and intergovernmental organizations publish vast quantities of grey literature, the importance of the diffusion and impact of this literature are rarely studied. Evidence from an investigation of the grey literature output of GESAMP, the Joint Group of Experts on the Scientific Aspects of Marine Environmental Protection (sponsored by the UN and several of the UN-family of organizations), indicated that the literature reached scientific readers and was cited. To determine whether that evidence was representative of international intergovernmental bodies, another intergovernmental organization devoted to marine environmental issues, namely, the Gulf of Maine Council on the Marine Environment (GOMC) was studied. GOMC, an American-Canadian partnership, has been working since 1989 to maintain and enhance environmental quality in the Gulf of Maine. Through its own publications and others resulting from studies conducted under contract or in cooperation with other organizations, GOMC provides a complex publishing history for investigation. Over 300 publications were identified and over 500 citations were located after extensive searching using several citation tools. Citation patterns for GOMC publications mirror the findings of the study of GESAMP; grey literature is cited over lengthy periods, but grey literature tends to be cited primarily by other grey literature. Although digital alerting and access tools are increasing in number and coverage, a reliance on grey literature as the primary means of publication continues to pose hurdles for influencing scientific research, public policy, and public opinion. While grey literature is common to organizations such as GOMC and GESAMP, the impact of this literature can be muted because of the limitations of its dissemination and perceptions of its quality.
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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 | 0.117 | 0.266 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.030 | 0.041 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.026 | 0.018 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.005 | 0.003 |
| 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; 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".