Investigating different typologies for the synthesis of evidence: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: The objective of this scoping review is to identify evidence synthesis types and previously proposed classification systems, typologies, or taxonomies that have guided evidence synthesis. INTRODUCTION: Evidence synthesis is a constantly evolving field. There is now a plethora of evidence synthesis approaches used across many different disciplines. Historically, there have been numerous attempts to organize the types and methods of evidence synthesis in the form of classification systems, typologies, or taxonomies. This scoping review will seek to identify all the available classification systems, typologies, or taxonomies; how they were developed; their characteristics; and the types of evidence syntheses included within them. INCLUSION CRITERIA: This scoping review will include discussion papers, commentaries, books, editorials, manuals, handbooks, and guidance from major organizations that describe multiple approaches to evidence synthesis in any discipline. METHODS: The Evidence Synthesis Taxonomy Initiative will support this scoping review. The search strategy will aim to locate both published and unpublished documents utilizing a three-step search strategy. An exploratory search of MEDLINE has identified keywords and MeSH terms. A second search of MEDLINE, Embase, CINAHL with Full Text, ERIC, Scopus, Compendex, and JSTOR will be conducted. The websites of relevant evidence synthesis organizations will be searched. Identified documents will be independently screened, selected, and extracted by two researchers, and the data will be presented in tables and summarized descriptively. DETAILS OF THIS REVIEW PROJECT ARE AVAILABLE AT: Open Science Framework https://osf.io/qwc27.
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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 | 0.360 | 0.307 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.026 | 0.024 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.009 | 0.012 |
| Research integrity | 0.014 | 0.012 |
| Insufficient payload (model declined to judge) | 0.071 | 0.026 |
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".