Episode 199 "Behind The Horror" with Dr. Lee Mellor
Bibliographic record
Abstract
My guest tonight for a late night conversation is Dr. Lee Mellor, PhD. He is a Canadian bestselling author, criminologist, and profiler specializing in homicide and sex crimes. He is also an accomplished singer-songwriter. The focus of our conversation is his recent book that was tailor made for this show...\\"Behind the Horror, True Stories That Inspired Horror Movies\\" We will talk about the real life events that inspired movies like Jaws, The Texas Chainsaw Massacre, M: A City Searches For a Murderer, Psycho, The Exorcist, Silence of the Lambs, and more. As the chair of the American Investigative Society of Cold Cases' academic committee, Mellor has reviewed unsolved homicides in Pennsylvania, Missouri, Ohio, and London, submitting offender profiles and recommendations to numerous police agencies. So dim the lights, settle in, and enjoy this late night conversation with Dr Lee Mellor! www.leemellor.com
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 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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.289 | 0.002 |
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; both teacher heads agree on what is shown here.
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".