Open Synthesis: Open Science in Evidence Synthesis (second speaker)
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
Slides to the session "Open Synthesis: Open Science in Evidence Synthesis" by Dr David Moher. Further details of the workshop can be found here: https://evidencesynthesisireland.ie/opensynthesis. Dr David Moher is a senior scientist, clinical epidemiology program, Ottawa Hospital Research Institute, where he directs the centre for journalology (publication science) (http://www.ohri.ca/journalology/). Dr Moher is also an Associate Professor, School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, where he holds a University Research Chair. Dr Moher holds an MSc in epidemiology and PhD in clinical epidemiology and biostatistics. Dr Moher has been involved in developing the science of how to optimally conduct and report systematic reviews for most of his professional career. Another part of his research has focused on how best to develop reporting guidelines. He spearheaded the development of the CONSORT statement and the PRISMA statement. He has been actively involved in the development of many other reporting guidelines and is part of the EQUATOR Network. Dr Moher leads an active program investigating predatory journals and publishers. More recently Dr. Moher led a program to develop core competencies for scientific journal editors. He is actively developing a program to investigate alternatives to current incentives and rewards in academic medicine. Dr Moher has been recognized several times as a Clarivate Analytics Highly Cited Researcher (Web of Science). The presentation was part of the Open Scholarship Week 2020. It can be viewed at https://www.youtube.com/watch?v=fANpI4xX-lk
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.
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchOpen science Domain: Methods · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Open science Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
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 | 0.104 | 0.265 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.023 | 0.003 |
| Open science | 0.035 | 0.017 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.324 | 0.103 |
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