‘Grey literature’ in systematic reviews and evidence syntheses on Environmental Health & Toxicology topics: a survey protocol
Bibliographic record
Abstract
Background Systematic reviews and other types of evidence syntheses use rigorous methods to identify and synthesise all of the relevant evidence to answer a question. A robust search strategy is crucial to conducting a high quality evidence synthesis, as an inadequate search may miss relevant evidence and produce biased findings. Methodological recommendations endorse searching for ‘grey literature’, i.e., reports published outside of traditional commercial publishing. However, there is currently a lack of clarity about what is considered to be the relevant types of grey literature and how to deal with it.Objectives This survey will aim to answer three questions: (1) How do those conducting evidence syntheses on environmental health and toxicology (EHT) topics understand ‘grey literature’; (2) Where do they search for grey literature?; (3) How do they deal with included grey literature evidence?Methods Participants will be individuals who conduct systematic reviews and evidence syntheses in EHT, without restrictions on: previous experience in conducting evidence synthesis, age, gender or geographic location. Survey will be disseminated via relevant professional organisations, systematic review and evidence synthesis organisations, and social media. It will consist of a mix of demographic and content questions. Descriptive statistics will be reported as frequencies and percentages; free-text responses will be analysed thematically.Ethics The survey was exempted from a requirement to undergo ethics review. The first page of the survey will provide the respondents with information about the project and ethics approval; respondents will provide consent by clicking on a button to advance to the survey.
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.
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 | 0.236 | 0.180 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.089 | 0.036 |
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".