What We’re Attending: ISMTE North America Conference
Bibliographic record
Abstract
Ethan's ItineraryIt's been 6 years since I've gone to ISMTE's Annual Conference.I also want to add that I've never been to Montreal (or Canada in general) so I am certainly going to take advantage of everything in front of me, sessions and otherwise.Now, four days of discussions can be quite daunting, and a new city can be, too.If you are looking at the huge slate of topics and wondering what to sign up for, allow me to throw my hat in the ring.The same goes for potential ideas once the meetings are over and you are considering how to use your free time.I will start by saying, Wednesday August 6 has a powerful list of sessions.Two that I have my eye on are "Breakout: Best Practices on Cascading and Transferring Rejected Manuscripts" and "Breakout: That Journal Article Published.Now What?"These represent two areas that many publishers struggle to master.Another thing they share is that a key pitfall for both, many times, is outside of our control.For "Best Practices on Cascading and Transferring Rejected Manuscripts", this is the natural progression of a movement by publishers over the past decade or so (maybe longer!).After creating a premier journal, publishers flock to bundling and creating sister publications that allows them to both capture more submissions and to sell them together at a slightly higher price.Unlike before, submissions from one can strengthen the other.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.121 | 0.055 |
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".