Bibliographic record
Abstract
Multiple blogs focus on autism spectrum disorder (ASD), but special educator Catherine Pascuas takes a new approach to sharing insights and resources on treating kids and young adults with autism. On The Autism Show Podcast, the consultant interviews specialists in the field—including several SLPs who are ASHA members—on relevant topics. Then she edits and produces the interviews into a series of podcasts. A new episode comes out each Tuesday and lasts approximately 20 minutes. You can subscribe to the podcast through iTunes or Stitcher. Although Pascuas creates each podcast with parents as the primary audience, she also asks the professionals she interviews to share advice for other pros in the field. Three recent episodes showcase SLPs with expertise in ASD. Heather MacKenzie, PhD, is a Canadian speech-language pathologist and international affiliate of ASHA. She writes books and programs on helping children manage their behavior, thinking and emotions. In the podcast, MacKenzie discusses tips for teaching self-regulation skills. Episode 38: Heather MacKenzie Karen Kabaki-Sisto, MS, CCC-SLP, helps children with autism improve their communication abilities. In 2015, she created an app to empower people with autism to start, maintain and end natural, flowing conversations. Episode 44: Karen Kabaki-Sisto Linda Barboa, PhD, CCC-SLP, holds degrees in speech-language pathology and audiology, psychology and early childhood education. She worked as a special education director, director of a center for autism and university professor. She co-authored Stars in Her Eyes: Navigating the Maze of Childhood Autism and presents programs to professionals across the country. Episode 48: Linda Barboa
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.086 | 0.017 |
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".