Are Keynote and Invited Speakers at State Behavior Analytic Conferences Experts on Their Presentation Topics?
Bibliographic record
Abstract
Abstract Board Certified Behavior Analysts® (BCBA®s) must acquire 32 continuing education units (CEUs) every two years. One way BCBAs obtain CEUs is by attending their state chapter conferences, which feature keynote speakers and invited speakers who disseminate information in their respective areas of presumed scientific expertise. This study evaluated 735 CEU presentations provided by keynote and invited speakers at state conferences in the United States for 2021, 2022, and 2023. For each keynote and invited presentation, researchers used Google Scholar to count the speakers’ (a) peer-reviewed publications on their presentation topic and (b) total peer-reviewed publications. In part, the results across all three years indicate that 31% of speakers had zero topic-specific publications. Notably, the percentage of speakers with zero topic-specific publications concerningly increased across the three years. Results also indicate nearly 40% of speakers with zero topic-specific publications did not have any peer-reviewed publications. We discuss the potential implications of the findings and suggest actions for offsetting the current trend.
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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 | 0.035 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.050 | 0.010 |
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".