Bibliographic record
Abstract
Good afternoon and welcome to the 2023 ADEA Annual Session & Exhibition!I'm delighted to greet you today as we kick off ADEA's Centennial Celebration and 99th annual session.When we came together at this meeting last year, we were fresh from the traumas of COVID-19regrouping and still a little stunned.Today, I'm happy to say we are no longer in crisis mode.We are surviving and thriving and enjoying the ability to think and act rather than simply having to react.And it's made for a productive year.We've taken the lessons and innovations of a difficult time and repurposed them so that, as an organization, we are stronger and more agile than ever.You may have heard that 2023 marks ADEA's 100th anniversary!In planning for this milestone, we aimed to honor the past, celebrate today, and commit anew to the future of oral health education.For 100 years, ADEA has persisted as "The Voice of Dental Education," dedicated to developing better scholars, faculty, and leaders.Now, we're committing to staying at the forefront of new thinking and innovation to carry us into the next century and beyond.We kicked off ADEA's Centennial celebration earlier this year with a number of branding and informationsharing activities.We displayed our Centennial logo on our ADEA website and elsewhere.We hailed the moment in a press release, and we relaunched our "I Am ADEA" campaign, placing our members front and center to tell our ADEA story.Additional plans include: S8
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.105 | 0.037 |
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".