Unlock the Genius Within: Neurobiological Trauma, Teaching, and Transformative Learning
Bibliographic record
Abstract
ere, Daniel S. Janik, MD, PhD, argues replacing education and teaching with non-traumatic, curiosity-based, discovery-driven, and mentor-assisted transformational learning. Unlock the Genius Within is an easy read that explains-in conversational manner-the newest ideas on neurobiological and transformational learning beginning with what's wrong with education and ending with a call for reader participation in developing an applying neurobiological learning and transformational learning theory and methodology. Janik draws extensively from his own experiences first as a physician working with psychological recovery from trauma, and then as an educator and linguist in applying neurobiological-based transformational learning in clinics, classrooms, and tutoring. Features:· Descriptions of classical and contemporary research alongside allusions to popular movies and television programs· Suggested further readings· Neurobiological learning web resourcesThroughout this book, the author incorporates humor, wisdom, and anecdotes to draw readers into traditionally incomprehensible concepts and information that demonstrates transformational learning. It will be of interest to teachers (postsecondary, secondary, and ESL), administrators, counselors, parents, students, and medical researchers. http://www.rowmaneducation.com/ISBN/1578862914
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".