CSE 2023 Annual Meeting: Reflecting on Community: Opening Borders in Scholarly Publishing
Bibliographic record
Abstract
The 2023 CSE Annual Meeting will take place April 29–May 2 in Toronto, Ontario. An exciting destination for attendees, the city of Toronto offers a variety of attractions, collaborative venues, multicultural communities, and culinary experiences. This year’s meeting will be a fully in-person event. We are excited to return to an in-person format in 2023 and will be offering multiple short courses, roundtable discussions, poster presentations, and networking options. Opportunities to attend CSE events virtually will be available during the CSE Fall Virtual Symposium and the CSE Webinar and Connect events throughout the year. As Program Co-Chairs, we are thrilled not only about the destination of this year’s meeting but also about the scope of content currently being developed. The scholarly publishing industry has experienced rapid evolution in recent years. As we look to the future, the resources of CSE continue to be invaluable to scientific communication education, networking, and engagement. The theme for the 2023 annual meeting is “Reflecting on Community: Opening Borders in Scholarly Publishing.” Part of our inspiration for this theme comes from the city of Toronto itself. The charming location boasts an inspiring motto: “Diversity Our Strength.” The CSE Program Committee has been working with individuals who submitted session proposals and moderators to develop an excellent slate of educational sessions around this theme. The program for the annual meeting will cover topics such as open access; managing society and publishing relationships; diversity, equity, inclusion, and accessibility; ethics and research integrity; as well as other critical topics […]
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 | 0.023 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.022 | 0.013 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.064 | 0.034 |
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 source (direct Gemma or distilled Codex), 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".