Call for Papers 8 Committee Reports Awards
Bibliographic record
Abstract
Summer seems like it has zipped by and Fall Quarters and Semesters are just around the corner for all of us. Not only does that mean we are all revving up for the return of the student hordes, but it is also time to start thinking about next year’s ASEE Annual Conference in Vancouver. Be on the lookout for the call for papers from this year’s Program Chair, Mike White. I am already getting excited about the meeting and the location as well! Before jumping into what has been happening since the conference in Louisville, I wanted to thank one more time all of the attendees and contributors for making the ELD sessions in Louisville so successful. Despite competing with another big conference for the second straight year and losing some attendees due to travel budget cuts, we again had a respectable turnout of the ELD membership. The session evaluations from Louisville were universally positive and the Lightning Talks experiment for the Get Acquainted Session was very well received. Overall, all sessions were again rated between “Good (4) ” and “Excellent (5). ” There was, of course, also constructive criticism received and passed on to this year’s Program Chair and Program Planning Committee. There is always room for improvement and the evaluations are definitely being taken into account for this year’s planning. While I am in the thanking mode, thanks again to the moderators, the Mentoring Committee, the Publications Committee, the Development Committee, the Directors, and the Program Planning Committee for taking on some so much of the conference workload…the conference would not have come off without you. For a list of the current officers and committee chairs, see
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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.020 | 0.006 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.755 | 0.794 |
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".