From “Grey Literature” to “Specialized Resources”: Rethinking Terminology to Enhance Grey Literature Access and Use
Bibliographic record
Abstract
Gambling Research Exchange (GREO) is an independent Knowledge Translation and Exchange (KTE) organization that aims to reduce harm from gambling. GREO curates and maintains a digital library of credible gambling information, most of which is grey literature. Several stakeholder groups use this library, including policy makers, researchers, treatment providers, regulators, and gambling operators. In order to meet knowledge needs, GREO both manages and produces grey literature, and maintains a research data repository for use by the gambling studies community. In keeping with the Open Science movement, the goal of the library is to provide timely and relevant evidence in formats accessible to diverse audiences, which can be used to inform decision-making, research, treatment, and policy direction. This paper documents how GREO’s digital library reorganized its search interface and document types and adopted accessible terminology so that complex research findings could extend beyond the academic community to broader audiences. Beginning in 2017, we assessed the existing library’s terminology and document types for accessibility and credibility. The first step was to rename the library from “Knowledge Repository” to “Evidence Centre”, a term that resonated more with non-academic audiences. Similarly, in 2018, we renamed the “Grey Literature” collection to “Specialized Resources” so that it is readily understood. Since the collection had grown considerably, we divided the single “Grey Literature” resource type into ten searchable categories to help direct users to the most appropriate resource formats. Examples include white papers, reports, visual tools, and instructional resources. A recent change in our funding model necessitated a further transition from a focus on Ontario, Canada to international audiences. Using examples drawn from a recent focus on gambling in Great Britain, this paper demonstrates how the GREO Evidence Centre has become increasingly accessible to wider audiences since 2017 to more effectively address their information needs.
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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 | 0.243 | 0.446 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.063 | 0.046 |
| Science and technology studies | 0.010 | 0.028 |
| Scholarly communication | 0.046 | 0.069 |
| Open science | 0.008 | 0.045 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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".