Investigating different typologies for the synthesis of evidence: a scoping review protocol
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
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 The search strategy will aim to locate both published and unpublished documents utilizing a three-step search strategy.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.
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 | 0.232 | 0.835 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.024 | 0.013 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.012 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 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