Open Synthesis: Open Science in Evidence Synthesis (fourth speaker)
Bibliographic record
Abstract
Slides to the session "Open Synthesis: Open Science in Evidence Synthesis" by Emma Thompson. Further details of the workshop can be found here: https://evidencesynthesisireland.ie/opensynthesis. Emma Thompson is the Advocacy and Partnership Officer within the Cochrane Executive Team where she coordinates Cochrane’s advocacy activities and supports the work of organizational strategic partnerships. Recently, she has developed and started work on a series of advocacy priorities for Cochrane – which includes campaigning for research integrity and for high-quality evidence synthesis in health decision-making. She began her career as a science journalist, before moving into communications and advocacy roles for non-profits focused on health and environmental issues at the EU level. 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
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 | 0.190 | 0.347 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.014 | 0.023 |
| Insufficient payload (model declined to judge) | 0.228 | 0.070 |
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".