Open Synthesis: Open Science in Evidence Synthesis (second speaker)
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.
How this classification was reachedexpand
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 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 | 0.170 | 0.308 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.014 | 0.022 |
| Insufficient payload (model declined to judge) | 0.237 | 0.080 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".