Bibliographic record
Abstract
The EDDA Study Designs Taxonomy (v2.0) was developed by the Evidence in Documents, Discovery, and Analytics (EDDA) Group: Tanja Bekhuis (Principal Scientist); Eugene Tseytlin (Systems Developer); Ashleigh Faith (Taxonomist); Faina Linkov (Epidemiologist). This work was made possible, in part, by the US National Library of Medicine, National Institutes of Health, grant no. R00LM010943. Foundational research is described in Bekhuis T, Demner Fushman D, Crowley RS. Comparative effectiveness research designs: an analysis of terms and coverage in Medical Subject Headings (MeSH) and Emtree. Journal of the Medical Library Association (JMLA). 2013 April;101(2):92-100. PMC3634392. Coverage of the terminology appearing in JMLA was extended with terms from MeSH, NCI Thesaurus (NCI), Emtree, the HTA Database Canadian Repository [international repository for health technology assessment], and Robert Sandieson's synonym ring for research synthesis. Collected terms were enriched with terms from the NCI Metathesaurus. Variants include synonyms for preferred terms, singular and plural forms, and American and British spellings. Definitions, if they exist, are mainly from MeSH, NCI, Emtree, and medical dictionaries. The EDDA Study Designs Taxonomy by Tanja Bekhuis and Eugene Tseytlin is licensed under a Creative Commons Attribution–NonCommercial–ShareAlike 4.0 International License.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".