COURSE TITLE: SYNDROME MIX: UNDERSTANDING THE KIDS IN IT NO OF CREDITS: 2 QUARTER CREDITS WA CLOCK HRS: 18 [semester equivalent = 1.33 credits] CEU HRS: 18**
Bibliographic record
Abstract
Every year, more and more students enter our classrooms diagnosed with ADHD, depression, Asperger's, anxiety, FAS, and other conditions in the "syndrome mix". And, if that's not challenging enough, these students can struggle with multiple conditions simultaneously, and often one of the individual conditions can mimic or exacerbate another. Take a student with a learning disability in math, throw in some ADHD with hyperactivity and, well... you know all too well, it makes life and learning difficult. As experienced educators we usually know when one of our students is challenged by factors seemingly beyond their control. These students- who are impacted by neurological and other factors-can seriously disrupt their own learning, as well as their peers in your classroom. This course will survey current research/practice to inform teachers about the most common student disorders- so they can better understand their students ' needs. The course text is $15 plus shipping, available from Amazon.com. Participants registering for clock hours do not need to obtain the book.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.692 | 0.468 |
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".